System and method for assessing and optimizing master data maturity

ABSTRACT

A system for assessing and optimizing master data maturity for an enterprise master data architecture. The master data maturity analysis system generates a master data maturity matrix for individual master data users. Each unique master data user&#39;s master data maturity matrix is used to generate master data matrices for other hierarchical entities for an enterprise such as lines of business and/or the enterprise as a whole. Master data maturity matrices are stored over time and tracked to assess and optimize the master data maturity and improvement of the master data technology vertically and horizontally across all enterprise hierarchies and business components.

CROSS REFERENCE TO RELATED APPLICATION

The present application claims priority to U.S. Provisional PatentApplication No. 62/454,291, entitled SYSTEM AND METHOD FOR ASSESSING ANDOPTIMIZING MASTER DATA MATURITY, filed on Feb. 3, 2017, the entirety ofwhich is incorporated by reference hereby.

DESCRIPTION OF THE RELATED TECHNOLOGY

In an enterprise, data falls into three types: master data, transactiondata, and inventory data. Typical master data classes include materialand product master data, supplier and customer master data, and masterdata regarding employees and assets. Transaction data, which describesbusiness transactions and represents input and output of businessprocesses, is constantly created in the course of business activities.Transaction data references master data and indicates changes toinventory data. Examples comprise purchase orders, invoices and shippingnotes. Inventory data refers to stock and account levels, e.g. to bankaccount balances or reserved stock of finished goods.

Master data refers to the characteristics of core business objectswithin an enterprise. Master data is the single source of information orthe ‘golden record’ that consists of uniform set of identifiers andstandard definition of attributes that describe core entities of anenterprise such as customers, suppliers, prospects, locations, products,etc. Master data is high value information that an organization usesrepeatedly across many lines of businesses (LOBs) and businesscomponents, and it supports key business processes and transactionsthroughout the entire enterprise.

Master data is managed as objects and attributes by a discipline knownas master data management (MDM), which generally refers to a combinationof hardware, software, policies and processes. According to ANSI/IEEEStandard 1471-2000, an architecture is defined as the “fundamentalorganization of a system, embodied in its components, theirrelationships to each other and the environment, and the principlesgoverning its design and evolution.” The information architecture asinformation system sub-architecture is often referred to as consistingof two parts, a master data model describing a company's key businessobjects as well as the relationships between them on a conceptual level(e. g. in a data object model) and an application architecturecomprising the entirety of a company's applications that create, store,and update instances of the entity types defined in the conceptualmaster data model. The enterprise master data architecture is aninformation architecture whose scope is restricted to a specific datatype (master data).

The general perception is that master data management (MDM) solutionsare the cure-all for master data in an enterprise. Yet unliketransaction data and inventory data, time frequency (time dependency ofdata) and change frequency (updates or changes and volume) growth ofdata over time are all comparatively low, but the independence of masterdata identities is very high.

Thus managing master data is challenging since data is usually scatteredthroughout the enterprise without consistent view in many systems.Fragmentation occurs as a result of the data being generated andmaintained by applications such as enterprise resource planning (ERP),customer relationship management (CRM) and other commercially availableoff-the-shelf (COTS) packages.

Delays and exponential cost increases in deploying MDM solutions hasmade customers wary of master data management solution(s) leading manyto establish evaluation processes to determine the effectiveness of theuse of master data. There are a several maturity models for datamanagement including master data management (MDM), data quality,information management, data governance, etc. Almost all such maturitymodels are focused on the software solutions that fall under eachcategory and do not measure the effective use of the actual data that isgenerally considered the life blood of such initiatives. They are also“linear” and “descriptive”, usually applied for “as-is” assessmentswhere the current capabilities are assessed with respect to givencriteria and are frequently used as a simple report that does nothingbeyond assigning a “maturity level” to an organization. Many existingmodels provide step-by-step recipes that simplify reality and lackempirical foundation. They also neglect the potential existence ofmultiple equally advantageous paths and focus on “linear” sequence oflevels towards a predefined “end state.”

MDM is an application-independent process that comprises designactivities on a strategic, organizational and at an information systemslevel. While data management has been investigated for a long time, MDMhas not been well established in the past.

For example, as shown in FIG. 15, conventional master data managementassessment can offer “top down” measurements of master data dimensions(y axis) that are by evaluators (e.g. managers or outside evaluators)and not the master data users themselves. As there is no systeminterface or master data maturity engine or tracking master datamaturity database, conventional systems provide a “snapshot” of, atbest, current levels and a future trend vector of a single userperspective in a company. Moreover, such measurements are much moresubjective as they are not based on actual master data usage by masterdata users, and thus put one or more degrees of ignorance between theactual master data usage and the measurement thereof, making report lessreliable and any information therefrom including subjective andimprecise.

Such platforms fail to accurately measure maturity of master data withina given enterprise, as different enterprises have various MDM solutionsand these data management solutions have differing functionality,deployment methods and features. There has been no method available tomeasure maturity levels of the actual master data within an enterprise.

SUMMARY

The following briefly describes embodiments to provide a basicunderstanding of some aspects of the innovations described herein. Thisbrief description is not intended as an extensive overview. It is notintended to identify key or critical elements, or to delineate orotherwise narrow the scope. Its purpose is merely to present someconcepts in a simplified form as a prelude to the more detaileddescription that is presented later.

As noted herein, extant MDM platforms have failed to appreciate that amaster data initiative in an enterprise should ensure consistent masterinformation across transactional and analytical systems, address keyissues such as data quality and consistency proactively rather than“after the fact”, and decouple master information from individualapplications while still providing for objective, measurable andactionable master data optimization.

The enterprise-wide management of master data is a prerequisite forcompanies to meet strategic business requirements such as compliance toregulatory requirements, integrated customer management, and globalbusiness process integration. Among others, this demands systematicdesign of the enterprise master data architecture. The currentstate-of-the-art, however, does not provide sufficient guidance forpractitioners as it does not specify concrete design decisions they haveto make and to the design options of which they can choose with regardto the master data architecture.

Briefly stated, various embodiments are directed to a system forassessing and optimizing master data maturity for an enterprisecomprising: a memory including a user database configured to storemaster data maturity data for a plurality of hierarchical entitiesincluding a plurality of unique master data users.

The system also includes a master data maturity engine configured tomeasure master data maturity for a plurality of hierarchical entitiesfor an enterprise based on master data maturity units, for example unitvalues such as master data maturity scores, generated for a plurality ofunique master data users, the master data maturity engine beingconfigured to generate a master data maturity matrix comprising aplurality of master data dimensions; wherein the system is configured togenerate the master data maturity matrix for each of the plurality ofhierarchical entities. The master data maturity matrix comprises themaster data maturity scores for each of the plurality of master datadimensions; wherein each master data dimension maturity score generatedby the master data maturity engine can be scored on a standardizedinterval scale measuring a plurality of ordinal levels for eachdimension. The system can be configured to calculate an overall masterdata maturity score from the plurality of master data dimension maturityscores for the master data maturity matrix.

In an embodiment, a line of business master data maturity matrix and anenterprise master data maturity matrix can be calculated from the masterdata user master data maturity scores, wherein each line of businessmaster data maturity matrix is generated from the master data maturityscores of the one or more unique master data users associated with thatline of business, and the enterprise master data maturity matrix isgenerated from the master data maturity scores of all the unique masterdata users for that enterprise. The system can be configured to allow auser to compare master data maturity scores for the hierarchicalentities.

The system can comprise a user interface configured to present each ofthe plurality of unique master data users with a master data evaluationinterface. The master data evaluation interface can include a surveycomprising, for each master data dimension, an input for an ordinalmeasure of the dimension, wherein the master data maturity enginegenerates the master data maturity score for the master data dimensionfrom the ordinal measure input, wherein the user interface is configuredto display a graphic of the master data maturity matrix.

In at least one of the various embodiments, described is a system,method, and computer program product for assessing and optimizing masterdata maturity configured for a system comprising: a network computer,including: a transceiver for communicating over the network; a masterdata maturity database configured to store master data maturity data fora plurality of hierarchical entities including a plurality of uniquemaster data users; a memory for storing at least instructions; and aprocessor device that is operative to execute instructions that enableactions, including: present each of the plurality of unique master datausers with a master data maturity interface configured to generate amaster data maturity matrix comprising a plurality of master datadimensions for each unique master data user; associate each of theunique master data users with a line of business and an enterprise;present, via the interface, each unique master data user with a masterdata evaluation survey evaluation which can comprise, for each masterdata dimension, an input for an ordinal measure of the dimension,wherein the master data maturity engine generates the master datamaturity score for the master data dimension from the ordinal measureinput; generate master data maturity scores for each of the plurality ofmaster data dimensions from the ordinal measure input; generate, foreach of the unique master data users, a master data maturity matrixcomprising the plurality of master data dimension maturity scores and anoverall master data maturity score; log the time the master datamaturity matrix is created; determine if a master data maturity matrixhas been generated for another master data user master data user loggedto the same enterprise; if so, generate an enterprise master maturitymatrix from each unique users' master data maturity matrix scores; logthe time the enterprise master data maturity matrix is created; log theline of business for each unique master data user to enterprise masterdata maturity matrix; and store each master data maturity matrix in themassive user database.

Described herein are embodiments of a method, a system, and a computerprogram product for determining the master data maturity of anorganization that deals with the master data use within an enterprise.The method includes receiving an input corresponding to a master dataevaluation, for example a survey such as a questionnaire. Thepre-determined model facilitates assessing the maturity level of theorganization across various goals. In addition to determining thematurity level across the goals, the pre-determined model facilitatesthe assessment of various key areas called dimensions associated withthese goals. In various embodiments of the invention, the dimensions areassigned a pre-defined ordinal level, wherein one or more questions ofthe questionnaire is associated with a corresponding goal.

These master data maturity dimensions help the enterprise evaluate itsexisting procedures and also take into account procedures that theorganization should employ to attain significant maturity levels.Further, since the list of dimensions associated with each key area isan exhaustive compilation, it helps in better assessment of theorganization, thereby guiding the organization by providing granularlevel details. Further, the responses provided by the employees of theenterprises are also validated to ensure data authenticity. Such anassessment at each level based on the testers' responses provides arobust and foolproof mechanism to determine the maturity level of theorganization.

In at least one of the various embodiments, the user taking thequestionnaire can set two types of master data maturity scores for eachquestion—a current score which embodies the current state or level ofmaster data use across one or more goals across one or more dimension,and a future target score which embodies the state of master data use isfor that goal and dimension and a discreet milestone therefor. This typeof dual scoring allows the user to not only assess the current state butalso track what changes the organization needs to make in order toachieve a different maturity level in their master data use.

Disclosed are embodiments that allow for master data users linked topredetermined business components such as lines of business (LOB) withinan enterprise, such as, marketing, sales, finance, HR, IT,manufacturing, products, etc., to take the maturity assessment of masterdata use within their individual organization and line of business. Thematurity scores and assessment can be tracked at these lines of businessas well as across all lines of businesses at a company or enterpriselevel.

“Master data user” as described herein refer unique users who take themaster data evaluation survey and generate a master data maturity masteruser matrix. A master data user can be any user in an enterprise whointeracts with master data and thus can provide accurate, individualizedinput reflecting their actual usage and assessment. “Hierarchical masterdata entities” or “hierarchical entities” as described herein refer toentities or business components in an enterprise that can comprise atleast one master data user, including master data user themselves. Themaster data user is the base unit or first order entity for hierarchicalentities, with other entities in an enterprise being entities thatinclude one or more unique master user entities. For example, one ormore master data users can associate themselves with a predefined lineof business, and that predefined line of business is a hierarchicalentity can include a plurality of master data users. Similarly, theenterprise is a hierarchical entity that includes all the master datausers. As described herein, any defined higher order hierarchical entitythat includes one or more master data users can have its own master datamaturity matrix generated by aggregating the master data maturitymatrices of unique master data users associated or linked to thathierarchical entity.

In embodiments, unique master data users can take the master datamaturity assessment as many times as needed or desired over time therebyallowing for monitoring progress of master data use within theirindividual lines of business as well as across the enterprise.

The method, the system, and the computer program product described abovehas a number of advantages:

-   -   First, such an assessment of the enterprise across multiple        goals and the dimensions facilitates the enterprise to        understand its current master data use procedures and its        improvement areas in a more detailed fashion. By having the        assessment, the tool assists the organization to focus its time        and resources in one particular dimension where the scoring is        low.    -   Second, the platform is dynamic and takes master data maturity        assessment to a prescriptive level. It indicates how to identify        desirable maturity levels and provides measurable guidelines on        improvement measures. Specific courses of action are suggested        by the model to reach a desired maturity level.    -   Third, setting current and future state of master data scoring        for each question allows the organization to monitor and track        progress from current state to a desired state over time, with        the advantage of multiple matrices for each master data user        tracking progress at a granular level.    -   Fourth advantage is that the assessment can be taken by many        lines of businesses within the same enterprise allowing for        visibility into areas of strength as well as weakness. This        allows for concentrated efforts in terms of effort and process        improvements to improve the use of master data in that area.    -   In addition, master data use is perceived differently by        different users. The consumer of master data, the data steward        responsible for ensuring the quality of master data, the        executive, the technologist who is responsible for integration,        etc., all have a different perspective on how the master data is        being utilized. Fifth advantage is that the invention allows for        different users within a line of business with differing roles        take the assessment thereby showing a comparison of areas of        strength and weakness. The model aggregates the results from        multiple users and shows the comparative maturity scores across        all users.

The master data maturity model focuses on factors driving evolution andchange allowing an enterprise to gain an informed, objective assessmentof the maturity of their organization or company. It helps identify,uncover, highlight and detail the strengths and weaknesses of theorganization's capabilities and to benchmark future levels oforganizational performance to develop a robust business case forinvestment and change management plan to deliver benefits. In addition,the user can compare with peers within and outside their individuallines of business or other defined business components allowing forsharing and collaborating on best practices and methods ofimplementation.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive embodiments are described with referenceto the following drawings. In the drawings, like reference numeralsrefer to like parts throughout the various figures unless otherwisespecified.

For a better understanding, reference will be made to the followingDetailed Description, which is to be read in association with theaccompanying drawings, wherein:

FIG. 1 is a system diagram of an environment in which at least one ofthe various embodiments can be implemented;

FIGS. 2-4 illustrate a logical architecture of a system and flowchartfor a process for master data maturity analysis in accordance with atleast one of the various embodiments.

FIG. 5 shows an embodiment of a client computer that can be included ina system such as that shown in FIG. 1;

FIGS. 6A-6B shows an embodiment of a network computer that can beincluded in a system such as that shown in FIG. 1 and a logical flowtherefor;

FIGS. 7-14B show user interfaces for generating and displaying masterdata maturity matrices in accordance with at least one of the variousembodiments.

FIG. 15 shows a graph representing conventional master data maturityplatform analysis.

FIG. 16 shows a graph representing master data user maturity analysisfor hierarchical master data users.

FIG. 17 shows interval scores for mapping ordinal answers.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Various embodiments now will be described more fully hereinafter withreference to the accompanying drawings, which form a part hereof, andwhich show, by way of illustration, specific embodiments by which theinnovations described herein can be practiced. The embodiments can,however, be embodied in many different forms and should not be construedas limited to the embodiments set forth herein; rather, theseembodiments are provided so that this disclosure will be thorough andcomplete, and will fully convey the scope of the embodiments to thoseskilled in the art. Among other things, the various embodiments can bemethods, systems, media, or devices. Accordingly, the variousembodiments can take the form of an entirely hardware embodiment, anentirely software embodiment, or an embodiment combining software andhardware aspects. The following detailed description is, therefore, notto be taken in a limiting sense.

Throughout the specification and claims, the following terms take themeanings explicitly associated herein, unless the context clearlydictates otherwise. The term “herein” refers to the specification,claims, and drawings associated with the current application. The phrase“in one embodiment” or “in an embodiment” as used herein does notnecessarily refer to the same embodiment, though it can. Furthermore,the phrase “in another embodiment” as used herein does not necessarilyrefer to a different embodiment, although it can. Thus, as describedbelow, various embodiments can be readily combined, without departingfrom the scope or spirit of the invention.

In addition, as used herein, the term “or” is an inclusive “or”operator, and is equivalent to the term “and/or,” unless the contextclearly dictates otherwise. The term “based on” is not exclusive andallows for being based on additional factors not described, unless thecontext clearly dictates otherwise. In addition, throughout thespecification, the meaning of “a,” “an,” and “the” include pluralreferences. The meaning of “in” includes “in” and “on.”

As used herein, the term “widget controller” refers to a computerprogram that can be operative on a client application. Widgetcontrollers can be downloaded and/or otherwise deployed to a clientapplication. Widget controllers can be arranged to be operative fordownloading content, monitoring master data user actions and input, orotherwise managing widgets located within client applications.

As used herein, the term “widget” refers to a user-interface elementlocated in the client application. Widgets can be invisible or visibleto users of the client applications. In some cases, a widget controllercan generate widget “on-the-fly” before deploying content into thewidget. Widgets can be adapted to reflect the operating environment ofthe client application that they are being hosted within. For example,in clients that support HTML, CSS a widget can be an HTML element suchas a DIV, P, or the like. For client application operative in a Javaenvironment, a widget can be a View object or Window object, and so on.

Illustrative Operating Environment

FIG. 1 shows components of one embodiment of an environment in whichembodiments of the innovations described herein can be practiced. Notall of the components can be required to practice the innovations, andvariations in the arrangement and type of the components can be madewithout departing from the spirit or scope of the innovations. As shown,system 100 of FIG. 1 includes local area networks (LANs)/wide areanetworks (WANs)—(network) 110, wireless network 108, client computers102-105, Master Data Maturity Analytics Server Computer 112, andBusiness Entity Information Server Computer 114.

At least one embodiment of client computers 102-105 is described in moredetail below in conjunction with FIG. 5. In one embodiment, at leastsome of client computers 102-105 can operate over a wired and/orwireless network, such as networks 110 and/or 108. Generally, clientcomputers 102-105 can include virtually any computer capable ofcommunicating over a network to send and receive information, performvarious online activities, offline actions, or the like. In oneembodiment, one or more of client computers 102-105 can be configured tooperate within a business or other entity to perform a variety ofservices for the business or other entity. For example, client computers102-105 can be configured to operate as a web server, an enterpriseserver serving enterprise client computers, a server host for aworkstation, or the like. However, client computers 102-105 are notconstrained to these services and can also be employed, for example, asan end-user computing node, in other embodiments. It should berecognized that more or less client computers can be included within asystem such as described herein, and embodiments are therefore notconstrained by the number or type of client computers employed.

Computers that can operate as client computer 102 can include computersthat typically connect using a wired or wireless communications mediumsuch as personal computers, multiprocessor systems, microprocessor-basedor programmable electronic devices, network PCs, or the like. In someembodiments, client computers 102-105 can include virtually any portablepersonal computer capable of connecting to another computing device andreceiving information such as, laptop computer 103, smart mobiletelephone 104, and tablet computers 105, and the like. However, portablecomputers are not so limited and can also include other portable devicessuch as cellular telephones, display pagers, radio frequency (RF)devices, infrared (IR) devices, Personal Digital Assistants (PDAs),handheld computers, wearable computers, integrated devices combining oneor more of the preceding devices, and the like. As such, clientcomputers 102-105 typically range widely in terms of capabilities andfeatures. Moreover, client computers 102-105 can access variouscomputing applications, including a browser, or other web-basedapplication.

A web-enabled client computer can include a browser application that isconfigured to receive and to send web pages, web-based messages, and thelike. The browser application can be configured to receive and displaygraphics, text, multimedia, and the like, employing virtually anyweb-based language, including a wireless application protocol messages(WAP), and the like. In one embodiment, the browser application isenabled to employ Handheld Device Markup Language (HDML), WirelessMarkup Language (WML), WMLScript, JavaScript, Standard GeneralizedMarkup Language (SGML), HyperText Markup Language (HTML), eXtensibleMarkup Language (XML), and the like, to display and send a message. Inone embodiment, a user of the client computer can employ the browserapplication to perform various activities over a network (online).However, another application can also be used to perform various onlineactivities.

Client computers 102-105 can also include at least one other clientapplication that is configured to receive and/or send content betweenanother computer. The client application can include a capability tosend and/or receive content, or the like. The client application canfurther provide information that identifies itself, including a type,capability, name, and the like. In one embodiment, client computers102-105 can uniquely identify themselves through any of a variety ofmechanisms, including an Internet Protocol (IP) address, a phone number,Mobile Identification Number (MIN), an electronic serial number (ESN),or other device identifier. Such information can be provided in anetwork packet, or the like, sent between other client computers, MasterData Maturity Analytics Server Computer 112, Business Entity InformationServer Computer 114, or other computers.

Client computers 102-105 can further be configured to include a clientapplication that enables an end-user to log into an end-user accountthat can be managed by another computer, such as Master Data MaturityAnalytics Server Computer 112, Business Entity Information ServerComputer 114, or the like. Such end-user account, in one non-limitingexample, can be configured to enable the end-user to manage one or moreonline activities, including in one non-limiting example, searchactivities, social networking activities, browse various websites,communicate with other users, or the like. However, participation insuch online activities can also be performed without logging into theend-user account.

Wireless network 108 is configured to couple client computers 102-105and its components with network 110. Wireless network 108 can includeany of a variety of wireless sub-networks that can further overlaystand-alone ad-hoc networks, and the like, to provide aninfrastructure-oriented connection for client computers 103-105. Suchsub-networks can include mesh networks, Wireless LAN (WLAN) networks,cellular networks, and the like. In one embodiment, the system caninclude more than one wireless network.

Wireless network 108 can further include an autonomous system ofterminals, gateways, routers, and the like connected by wireless radiolinks, and the like. These connectors can be configured to move freelyand randomly and organize themselves arbitrarily, such that the topologyof wireless network 108 can change rapidly.

Wireless network 108 can further employ a plurality of accesstechnologies including 2nd (2G), 3rd (3G), 4th (4G) 5th (5G) generationradio access for cellular systems, WLAN, Wireless Router (WR) mesh, andthe like. Access technologies such as 2G, 3G, 4G, 5G, and future accessnetworks can enable wide area coverage for mobile devices, such asclient computers 103-105 with various degrees of mobility. In onenon-limiting example, wireless network 108 can enable a radio connectionthrough a radio network access such as Global System for Mobilcommunication (GSM), General Packet Radio Services (GPRS), Enhanced DataGSM Environment (EDGE), code division multiple access (CDMA), timedivision multiple access (TDMA), Wideband Code Division Multiple Access(WCDMA), High Speed Downlink Packet Access (HSDPA), Long Term Evolution(LTE), and the like. In essence, wireless network 108 can includevirtually any wireless communication mechanism by which information cantravel between client computers 103-105 and another computer, network,and the like.

Network 110 is configured to couple network computers with othercomputers and/or computing devices, including, Master Data MaturityAnalytics Server Computer 112, Business Entity Information ServerComputer 114, client computer 102, and client computers 103-105 throughwireless network 108. Network 110 is enabled to employ any form ofcomputer readable media for communicating information from oneelectronic device to another. Also, network 110 can include the Internetin addition to local area networks (LANs), wide area networks (WANs),direct connections, such as through a universal serial bus (USB) port,other forms of computer-readable media, or any combination thereof. Onan interconnected set of LANs, including those based on differingarchitectures and protocols, a router acts as a link between LANs,enabling messages to be sent from one to another. In addition,communication links within LANs typically include twisted wire pair orcoaxial cable, while communication links between networks can utilizeanalog telephone lines, full or fractional dedicated digital linesincluding T1, T2, T3, and T4, and/or other carrier mechanisms including,for example, E-carriers, Integrated Services Digital Networks (ISDNs),Digital Subscriber Lines (DSLs), wireless links including satellitelinks, or other communications links known to those skilled in the art.Moreover, communication links can further employ any of a variety ofdigital signaling technologies, including without limit, for example,DS-0, DS-1, DS-2, DS-3, DS-4, OC-3, OC-12, OC-48, or the like.Furthermore, remote computers and other related electronic devices couldbe remotely connected to either LANs or WANs via a modem and temporarytelephone link. In one embodiment, network 110 can be configured totransport information of an Internet Protocol (IP). In essence, network110 includes any communication method by which information can travelbetween computing devices.

Additionally, communication media typically embodies computer readableinstructions, data structures, program modules, or other transportmechanism and includes any information delivery media. By way ofexample, communication media includes wired media such as twisted pair,coaxial cable, fiber optics, wave guides, and other wired media andwireless media such as acoustic, RF, infrared, and other wireless media.

One embodiment of Master Data Maturity Analytics Server Computer 112 isdescribed in more detail below in conjunction with FIG. 6A. Briefly,however, Master Data Maturity Analytics Server Computer 112 includesvirtually any network computer capable of master data maturity analysisand generating interfaces and interface objects as described herein.Computers that can be arranged to operate as Master Data MaturityAnalytics Server Computer 112 include various network computers,including, but not limited to personal computers, desktop computers,multiprocessor systems, microprocessor-based or programmable consumerelectronics, network PCs, server computers, network appliances, and thelike.

Although FIG. 1 illustrates Master Data Maturity Analytics ServerComputer 112 as a single computer, the embodiments are not so limited.For example, one or more functions of the Master Data Maturity AnalyticsServer Computer 112 can be distributed across one or more distinctnetwork computers. Moreover, Master Data Maturity Analytics ServerComputer 112 is not limited to a particular configuration. Thus, in oneembodiment, Master Data Maturity Analytics Server Computer 112 cancontain a plurality of network computers. In another embodiment, MasterData Maturity Analytics Server Computer 112 can contain a plurality ofnetwork computers that operate using a master/slave approach, where oneof the plurality of network computers of Master Data Maturity AnalyticsServer Computer 112 is operative to manage and/or otherwise coordinateoperations of the other network computers. In other embodiments, theMaster Data Maturity Analytics Server Computer 112 can operate as aplurality of network computers arranged in a cluster architecture, apeer-to-peer architecture, and/or even within a cloud architecture.Thus, embodiments are not to be construed as being limited to a singleenvironment, and other configurations, and architectures are alsoenvisaged.

In at least one of the various embodiments, Business Entity AnalyticsServer Computer 114 can be one or more computers arranged to providebusiness entity analytics, such as, a network computer like thatdescribed in conjunction with FIG. 6A, or the like. As described herein,Business Entity Analytics Server 114 can include a database of robustcompany/business entity data 304 and employee data to enrich companymaster data maturity databases as described herein. Examples of BusinessEntity Analytics Servers 104 are described in U.S. Pat. No. 7,822,757,filed on Feb. 18, 2003 entitled System and Method for Providing EnhancedInformation, and U.S. Pat. No. 8,346,790, filed on Sep. 28, 2010 andentitled Data Integration Method and System, the entirety of each ofwhich is incorporated by reference herein. In at least one of thevarious embodiments, Business Entity Analytics Servers 104 can includeone or more computers, such as, network computer 600 of FIG. 6, or thelike. Briefly, however, Business Entity Information Server Computer 114includes virtually any network computer capable of Business EntityInformation delivery. Computers that can be arranged to operate asBusiness Entity Information Server Computer 114 include various networkcomputers, including, but not limited to personal computers, desktopcomputers, multiprocessor systems, microprocessor-based or programmableconsumer electronics, network PCs, server computers, network appliances,and the like.

Although illustrated separately, Master Data Maturity Analytics ServerComputer 112 and Business Entity Information Server Computer 114 can beemployed as a single network computer, separate network computers, acluster of network computers, or the like. In some embodiments, eitherMaster Data Maturity Analytics Server Computer 112 or Business EntityInformation Server Computer 114, or both, can be enabled to serveinterfaces and content, respond to user interactions, track userinteraction with the master data maturity system, update widgets andwidgets controllers, or the like.

In at least one of the various embodiments, Master Data MaturityAnalytics Server 112 can be arranged to be in communication withBusiness Entity Information Server 114, Hosting Servers (not shown), orthe like.

In at least one of the various embodiments, Master Data MaturityAnalytics Server 112 can be one or more computers arranged to analyzeMaster Data maturity. In at least one of the various embodiments, MasterData Maturity Analytics Servers 114 can include one or more computers.In at least one of the various embodiments, Master Data MaturityAnalytics Server 112 and/or Business Entity Information Server 114 canbe hosted by hosting servers (not shown) including one or morecomputers, such as, network computer 300, or the like, that host one ormore types of content that are provided for master data users andenterprises. For example, hosting servers can include one or more webservers providing web sites, images hosting sites, CRM servers, or thelike. In at least one of the various embodiments, hosting servers can bearranged to integrate with Master Data Maturity Analytics Servers 114.

In at least one of the various embodiments, Master Data MaturityAnalytics Servers 114 can be arranged to integrate and/or communicatewith client devices or Business Entity Information Server 114 usingAPI's or other communication interfaces. For example, one contentprovider service can offer a HTTP/REST based interface to BusinessEntity Information Servers 114, client devices, or Enterprise servers.

In at least one of the various embodiments, master data MaturityAnalytics tools served from and/or hosted on Master Data MaturityAnalytics Servers 114, hosting servers, and Business Entity InformationServers 114 can be provided over network to one or more clientcomputers, such as, Client Computer 102, Client Computer 103, ClientComputer 104, Client Computer 105, or the like.

In at least one of the various embodiments, communication betweenBusiness Entity Information Server 114 and Master Data MaturityAnalytics Servers 112, and hosting servers and can include one moreevents that can correspond master data maturity analytics. Further,Master Data Maturity Analytics Servers 114 can be arranged tocommunicate directly or indirectly over network to the client computersusing one or more direct network paths, such as network path 111. Thiscommunication can include information associated with one or more eventsoccurring on the client computers.

One of ordinary skill in the art will appreciate that the architectureof system 100 is a non-limiting example that is illustrative of at leasta portion of at least one of the various embodiments. As such, more orless components can be employed and/or arranged differently withoutdeparting from the scope of the innovations described herein. However,system 100 is sufficient for disclosing at least the innovations claimedherein.

FIG. 2 represents a flow for system 200 for master data maturityanalytics in accordance with at least one of the various embodiments. Inat least one of the various embodiments, the system receives input froma plurality of unique master data users 201-1 . . . 201-n. In at leastone of the various embodiments, the unique master data users 201-1 . . .201-n are associated with an enterprise, and information about thatenterprise can that can be managed by the Master Data Maturity AnalyticsServers 112, Business Entity Information Servers 114, network computer300, or the like.

In at least one of the various embodiments, input that can be receivedfrom master data users 201-1 . . . 201-n can be processed in a masterdata maturity engine 204. The master data maturity engine 204 isconfigured to measure master data maturity for a plurality ofhierarchical entities 202, 204, 210 for an enterprise based on masterdata maturity unit values such as scores 206-1 . . . 201-n generated forthe plurality of unique master data users 201 . . . 201 n. Examples ofhierarchical entities for whom master data maturity is analyzed andscored includes the master data users 201-1 . . . 201-n, a line ofbusiness 202-1 . . . 202-n, and the enterprise 210. As described herein,the system is configured to use master data user's maturity scores 206-1. . . 206-n to generate scores and matrices for lines of business 202-1. . . 201-n the users belong to as well as enterprise scores and anenterprise master data maturity matrix 209 for the entire enterprise210.

As will be appreciated, one exemplary advantage of the technologydescribed herein is it more accurately obtains the real master datamaturity of an enterprise by “sampling” from multiple ‘points’ withinthe company—the unique master data users—as opposed to a single person,manager, or outside evaluator taking the assessment. One or a fewpersons taking the assessment and entering into a platform can giveskewed results and may not represent an accurate maturity level for thewhole company or even division or line of business. Embodiments of thetechnology as described herein allow assessments to be directly inputunder controlled conditions by master data users representing divisionsor groups, practitioners, decision makers, end users, indeed, up to andincluding all master data users and stakeholders in the enterprise. Allthe unique master data user maturity matrix scores are used to calculateand generate company-wide maturity trend scores while allowing foridentifying and comparing divisional/group hierarchical level maturityassessments.

In at least one of the various embodiments, the master data maturityengine 204 includes a comparator 208 that is configured to comparematurity matrix scores for different hierarchical entities across theenterprise. For example, in at least one of the various embodiments, thesystem can compare line of business master data maturity matrix scores207-1 . . . 207-n as shown in FIGS. 14A-14B herein.

Generalized Operation

The operation of certain embodiments will now be described with respectto FIG. 3A to FIG. 4. In at least one of various embodiments, processes300 and 400 conjunction with FIG. 3A to FIG. 4 can be implemented byand/or executed on a single network computer, such as network computer600 of FIG. 6A. Likewise, in at least one of the various embodiments,processes 300 and 400, or portions thereof, can be operative on one ormore client computers, such as client computer 500 of FIG. 5. However,embodiments are not so limited, and various combinations of networkcomputers, client computers, virtual machines, or the like can beutilized. Further, in at least one of the various embodiments, theprocesses described in conjunction with FIG. 3A-FIG. 4 and interfaces ofFIGS. 7-14 can be operative in system with logical architectures such asthose described in conjunction with FIGS. 1-2 and FIG. 6B.

In at least one of the various embodiments, master data maturity matrixinformation can be stored in one or more data stores, for laterprocessing and/or analysis. Likewise, in at least one of the variousembodiments, information can be processed as it is determined orreceived. Also, master data maturity matrix information can be stored indata stores, such as databases, for use as historical information and/orcomparison information.

FIG. 3A illustrates an overview flowchart for process 300 for masterdata maturity analysis in accordance with at least one of the variousembodiments. At block 302, in at least one of the various embodiments, amaster data user 301 logs into a master data maturity interface, forexample a secure web interface 303 with a web server configured topresent the master data maturity interface.

At block 304, in at least one of the various embodiments, the masterdata user is presented with a master data maturity interface configuredto generate a master data maturity matrix.

In an embodiment, the system maps or links hierarchical entity data tothe master data user. For example, at block 305, if the user or theenterprise has generated a unique master data profile, for example asshown in FIG. 3B or FIG. 3C, FIGS. 7-14, the system saves or maps thehierarchical entity data with the unique master data user, such as theenterprise's company name and line of business for that master datauser. Then the system can link or map the master data user's master datamaturity matrix to that hierarchical entity data.

In at least one of the various embodiments, as shown in FIG. 3B, thesystem is configured to allow a master data user 301 to create a profilethat links them to an enterprise. As shown in FIG. 3B, after the masterdata user 301 is presented with a master data maturity interface atblock 304, at block 320 the system presents the master data user 310with a registration page to generate a unique master data profile. Theuser can create, via the interface, a username and password, as well asenter a company name and select a line of business, thereby linking themaster data user with these hierarchical entitles (e.g. the enterpriseand line of business). For example, the system can generate a uniquetoken that associates the enterprise account with the company name andinformation. In at least one of the various embodiments, the master datauser can link the enterprise data with a company ID (e.g.: a DUNS ID)for a business entity information service that includes robustfirmographics, for example, that links the company with informationobtained from one or more Business Entity Analytics Servers 114.Examples of a business entity information services and Business EntityAnalytics Servers 114 are described in U.S. Pat. No. 7,822,757, filed onFeb. 18, 2003 entitled System and Method for Providing EnhancedInformation, and U.S. Pat. No. 8,346,790, filed on Sep. 28, 2010 andentitled Data Integration Method and System, the entirety of each ofwhich is incorporated by reference herein. At block 321 the master datauser 301 is then sent an activation e-mail to activate the profile andan enterprise account with the master data maturity system. As shown atblock 322, the master data user or the system can invite any number ofother master data users 201-n in the enterprise, for example via anemail or other suitable electronic communication. Each of master datausers can then log into a master data maturity interface, for examplevia the secure web interface 303, where the master data users canregister to the company master data maturity account. As the users arealready linked to the enterprise by virtue of the invitation from themaster data user creating the company account, at block 324 the userregisters to the enterprise account with a user name, password and lineof business. The system can then link the hierarchical entity data withthe unique master user(s) as shown in block 305 of FIG. 3A, namely theenterprise's company name and line of business for that master data userand continue to use the master data maturity interface as describedherein.

In an embodiment, the master data maturity system offered by a businessentity information service that includes robust firmographics, forexample, a service hosting Business Entity Analytics Servers 114. Asshown in FIG. 3C, an administrative user 319 or enterprise user havingan existing enterprise account with the business information entityservice can create an associated enterprise account or enterpriseprofile for master data maturity analytics. At block 330 theadministrative user 319 is presented with a master data maturityadministrative interface. The operational flow proceeds to block 331,where the administrative user can create, via the interface, a companyaccount, and the system can generate a unique token that associates theenterprise account with the company name and information from thebusiness entity information service. The account can be thus created andassociated with a company ID (e.g.: a DUNS ID) for a business entityinformation service that includes robust firmographics, for example,that links the company with information obtained from one or moreBusiness Entity Analytics Servers 114. At block 332 the system or theadministrative user can then send invitations, for example via an emailor other suitable electronic communication, to one or more master datausers associated with the enterprise, for example, those registered toor logged with the business entity information service. The master datauser 301 can invite other master data users as described herein. Themaster data users then can log into a master data maturity interface,for example via the secure web interface 303, where the master datausers can register to the company master data maturity account. As themaster data user(s) are already linked to the enterprise by virtue ofthe invitation from the master data user creating the company account,at block 324 the user registers to the enterprise account with a username, password and line of business. At block 325 the master data usercan then send or enter an activation e-mail, which may already be knownto the system, to activate the master data user profile to theenterprise account with the master data maturity system. The system canthen link the hierarchical entity data with the unique master user(s) asshown in block 305 of FIG. 3A, namely the enterprise's company name andline of business for that master data user and continue to use themaster data maturity interface as described herein.

Returning to FIG. 3A, in at least one of the various embodiments, themaster user is presented, via the interface, an evaluation surveyconfigured to generate a master data maturity matrix comprising aplurality of master data dimensions for each unique master data user. Inan embodiment, the master data maturity matrix includes a plurality ofmaster data goals, and each master data goal comprising one or more ofthe master data maturity dimensions.

In an embodiment, the master data evaluation survey comprises, for eachmaster data dimension, an input for an ordinal measure of the dimension.For example, at block 306, the master data evaluation survey comprises aquestionnaire that includes, for each master data dimension, apredetermined question and a plurality of predetermined ordinal answersfor each question, and the user interface is configured to accept aselected answer as the ordinal measure input and generate a master datamaturity score for the dimension. An exemplary survey questionnaireillustrating exemplary predetermined questions for 24 master datadimensions that measure 6 master data goals is shown at Tables 1A-1F.Each predetermined question has 6 predetermined answers which the masterdata user can select one answer. The answers are ordinal inputs whichare used to generate master data scores for each master data dimension.

TABLE 1A-1 Goal Dimension Question Levels Vision Vision What is yourcompany vision for Master Data? There is no vision for Master Data Some“grass-roots” initiatives are emerging from within the organization thatdescribe how to use Master Data Some Master Data initiatives exists butare IT led and lack sufficient business involvement Master Data visionin place One unifying vision for Master Data across the organization Oneunifying vision for Master Data that clearly links Master Data tobusiness results

TABLE 1B-1 Goal Dimension Question Levels Strategy Strategy What is yourstrategy for managing Master Data No strategy in place requirements? Donot have a strategy and little or no organized actions to address MasterData Strategy is mostly reactive Strategy is proactive Strategy isconsistent and integrated across the org Strategy has regular fundingand is focused on ongoing improvement of Master Data Unique IDs What isyour strategy for assigning Unique IDs to Do not see a value in UniqueIDs Master Data assets such as Customer No Standardization of Unique IDsUnique IDs are maintained by individual systems or domains Centralrepository with unique ID and local mapping in place Central repositorywith unique ID and maintained by domain Adheres to a strict policy inregards to unique IDs for customers Enrichment What is your strategy forenriching Master Data from Limited use of existing data external as wellas internal data sources? No strategy in place to enrich existingrecords with additional attributes Some systems and domains requireadditional attributes. Strategies in place for adding these on anas-needed basis Few enrichment attributes are automatically sourced andupdated Enrichment is part of the overall master data plan. Sourcing ofadditional attributes is limited Enrichment plays a key role in thebusiness results. Strategies and processes are in place for sourcing alladditional attributes

TABLE 1B-2 Goal Dimension Question Levels Strategy HierarchiesCommercial entities such as Customers and Vendors Limited or no need formaster data can have relationships (legal, geography, location, Nohierarchies are factored in master data creation and distribution etc.)to other entitles which can be represented in a Hierarchies are addedafter the fact. hierarchies. Such information provide critical claritySome areas of the business are capable of including hierarchical intothe opportunity or risk associated with a information for customerrelationship. What is your strategy for managing Strategy in place tosupport lookup of some hierarchical hierarchical information?information for customer Strategy in place for entities such as customeror vendor to support several hierarchical arrangements based on aparticular classification schema (legal entity level Consistency What isyour strategy for maintaining consistent Consistency is not necessaryfor consumption schema definition for commercial entities such Dataconsistency is non-existent as customers and vendors? Consistency ofschema There is consistency in attributes across domains but disparateschema is used Schema and attributes are consistently maintained foreach master data entity such as customers Strategy is in place to manageand maintain consistent schema and attributes. The taxonomy of masterdata is also tied into governance processes Data How is external datasourced and converted to No external data is used. All data is createdinternally Sourcing Master Data? Data Lists are purchased on a as neededbasis Data sourced from a single vendor and is not centralized SourceData is sourced from multiple vendors individually by different groups.Some of the sourced data becomes Master Data Strategy in place tomonitor and update Master Data from all the sourced data as it comes inSourced data is immediately converted to Master Data or in some casesexisting Master Data is augmented with sourced data

TABLE 1C-1 Goal Dimension Question Levels Culture & Culture What is yourcompany culture in regards to Master Data? No one is responsible forMaster Data Execution Master Data is created on an as needed basis withno or few rules/standards Good understanding of Master Data as an assetMaster Data is treated at the organizational level as critical forsuccessful mission performance Master Data is treated as a source ofcompetitive advantage Master Data is seen as critical for survival in adynamic and competitive market. Data is “talk of the town” ExecutionWhat is your Master Data Execution status? Version of the truthunnecessary No version of the truth Multiple versions of the truthMultiple versions of the truth with processes in place Single version ofthe truth Single golden record of master data managed and maintainedMetrics What is/are your Master Data Metrics? No metrics necessary NoMetrics for Master Data Quality goals Starting to develop metrics to setMaster Data Quality goals but there is no unified metrics schemeSuccessful in using metrics at the domain level Using metrics to measuresuccess across data domains Metrics as the basis for management andfurther investments in Master Data

TABLE 1C-2 Goal Dimension Question Levels Culture & Change How are youidentifying and executing to Master Data Customer and Vendor list do notneed change Execution management changes?s Master Data is updated onlywith new additions Master Data changes are updated only when there is aproblem Master Data changes are checked and updated periodically Masterdata is monitor for changes and updates are propagated to systems whenthey occur Master Data changes are continuously monitored and impactedsystems are updated immediately Level of What level of importance doesyour organization give Not considered important Importance towardsMaster Data Quality? Organization understand the need for commonstandards Organization takes steps towards cross-department sharing toachieve operational efficiency Full business sponsorship and involvementBusiness impact analysis of data flaws is common Senior management seesinformation as a competitive advantage and exploits it to create valueand increase efficiency

TABLE 1D-1 Goal Dimension Question Levels Master Data Awareness Howaware is your organization in regards No Awareness Quality to MasterData Quality? Awareness is growing of poor data quality and offragmented and inconsistent information in key subject areas Dataquality concerns are perceived as mainly IT department's responsibilityData quality given considerable attention in the IT charter Data qualityis a prime concern of both IT and business In-depth quality analysis forboth objective and subjective quality attributes such as latencyCleansing What is your approach to Data cleansing Corrections happensporadically which includes standardization Data corrections occurperiodically but duplicates remain Data corrections are done regularly.However the siloed nature of Master Data does not eliminate duplicatesDedupe and Data cleansing are considered important and is considered tobe the responsibility of IT Data quality tools are in place forcleansing including duplicate identification Master Data Cleansingincluding duplicate identification and elimination are part of theongoing efforts and important part of all business processes AssessmentHow does your organization react to Master No capability for identifyingdata quality expectations nor are they Data Quality issues? documentedWait and see approach to data quality issues Data quality issues areaddressed after they occur (reactive) Capability for validation of datausing defined data quality rules. Methods for assessing business impactexplored. Data quality tools are regularly used on a project-by-projectbasis Proactive data quality efforts. Data quality is a prime concern ofboth IT and business. Regular measures and monitors for data quality atan enterprise level and across multiple systems. Unstructuredmission-critical information

TABLE 1D-2 Goal Dimension Question Levels Master Data Quality MonitoringWhat level of change monitoring does your Monitoring is unnecessaryorganization have for Master Data? No quality or updates to the data ischecked Master data is monitored for updates and quality after-the-factSome monitoring for updates and quality in place Data quality checks anddata updates are part of operational information systems Data updatesand Quality monitoring are included in regular business processesUpdates How does your organization handle Master Data updates are doneonly when needed Data Updates? Updates are not propagated across allsystems Updates to master data are made once an issue is discoveredUpdates happen actively but propagated only across some systems MasterData changes are monitored and updated as they occur Monitoring andUpdates are integral part of the operational ecosystem

TABLE 1E Goal Dimension Question Levels Technology, Integration What isyour Master Data Sourcing and No Integration in place nor necessarySearch & Integration process? Data collection & sourcing takes up mostof the time. Master data Distribution often siloed and most data is notintegrated. Changes to Master data are uncontrolled with no common datadefinitions Some data integration. Controls developed around changes ofmaster data definitions. Different guidelines and processes arounddefinitions and requirements gathering Integration of data silos allowsfor key data being available. Data management processes rationalized.Common data definitions Master Data solution covers multiple businessunits Internal and external data shared and readily available.Additional sources easily added. Master Data solution integrated withconsistent capabilities that includes management of customerDistribution How do your distribute your Master Distribution is donemanually or email Data into other systems? IT departments seekefficiencies through vertical silo consolidation (data warehouse) usingdata modelers and DB administrators Integrating new application systemstypically requires all data and processing integrity logic be replicatedto each consuming application. MDM solutions are in the process ofimplementation Master Data solution covers few data domain Flexible dataarchitecture - information as a service. Data Quality metrics embeddedin processes and systems. Data Quality approach adjusted when businessstrategy changes. Full fledged MDM solution implemented ComprehensiveMDM solution is implemented. Distributed of Master Data across varioussystems and applications is through Master Data-as-a-Service (M-DaaS)Matching How do you currently search? No Search and retrieval necessaryFinding Master Data involves manual lookup of a database or files Lookupusually with scripts is limited to each silo where master data ismaintained Search All master data can be searched using a centralizedframework. Some match logic in place. Complex match algorithms

TABLE 1F Goal Dimension Question Levels Governance & Practice What isyour organization's awareness and No awareness Stewardship practicetowards Governance Some awareness of the importance of InformationStrategy and requirements for Master Data? Data Governance Metrics areemerging to focus on retention for information Processes in place tovalidate data quality compliance. Implemented a process driven datagovernance framework that supports centralized business rules managementand distributed rules processing Enterprise data governance enables highquality master data sharing across the enterprise Governance What isyour current Governance policies No Governance necessary for MasterData? No Data Governance organization Informal governance role andpolicies exists. Information management policies and standards arepublished Governance organization in place. Standard processes toaddress data aspects of projects. Leadership is aware of the importanceof Data Governance and the impact on the performance of the organizationData governance in place Governance extended to business partners.Prevention is main focus. All demand and supply processes address dataaspects. Adherence to policies is enforced. Risk How do you handle Riskrelated to Master No risk management necessary Management Data? Effortto document risk associated with uncontrolled information assets hasbegun Risks of not managing information as corporate asset are wellunderstood Full compliance with information management policies andstandards Cross enterprise multi-domain governance in place Governanceis well established across the enterprise Stewardship What is yourapproach to Data Stewardship? No data stewardship necessary Do you havededicated Data Stewards? Do not have data stewards that haveresponsibility for Master data quality. Data owned at departmental levelStarting to develop a culture of data stewardship. Data stewardship andownership at departmental level Team of data stewards Centralized orfederated steward groups across the organization Well established

At block 307, in at least one of the various embodiments, the systemgenerates the master data maturity matrix 310 for that master data user.As shown in FIGS. 3A-4, the system generates master data maturity scoresfor each of the plurality of master data dimensions from the ordinalmeasure inputs, described herein as the selected answers to thepredetermined questions. For example, in Tables 1A-1F above, each masterdata dimension has 6 predetermined ordinal answers. When a master datauser inputs one of the six answers for each master data dimensionquestion, the system generates a master data maturity dimension scorebased on the answer. As shown in FIG. 17, each answer for the ordinallevel maps to an interval score. Master data users are asked to selectfrom six answers corresponding to six levels for each dimension. Theuser will also have the option to select in-between levels, for examplesuch as an interval between “Active” and “Proactive.” The systemgenerates a unit score of one point for each selection of levels, and ahalf-point is added to the previous level if the master data userselects “in-between” answers for the levels. For example, if the userselects “Active” for a dimension, they score 3 points, 4 points if theyselect “Proactive” and 3.5 points if they select the interval between“Active” and “Proactive.” The scoring begins at 0 and ends at 5. Forexample, selecting “Non-existent” scores a 0 and “Strategic” scores a 5.If the user chooses to ignore a dimension, the default score is “0.”Thus the system is thus configured to generate 11 possible intervalscores based on selected answers by the master data user, as shown onthe exemplary interval scale in FIG. 17.

The system is also configured to calculate an overall master datamaturity score from the plurality of master data dimension maturityscores for the master data maturity matrix 310. For example, an overallmaster data maturity score can be calculated as a mean or otheraggregated score. In an embodiment, the system is configured to generatea master data maturity trend score as the overall master data maturityscore for the master data user. In at least one of the variousembodiments, the master data maturity trend score is a statisticalscore. Exemplary models for statistical scoring of a maturity trendscore include a linear model selected from the group of linearregression, linear least-squares; or an iteratively reweighted leastsquares (IRLS).

A trend line can be calculated as straight line approximation, forexample by calculating a straight line on a scatter plot so that thenumber of points above the line and below the line is about equal (andthe line passes through as many points as possible). In an embodiment,the system can be configured to use a least square method to calculatethe overall master data maturity trend score. For example, the masterdata maturity engine is configured to apply a straight-line functionwith the data plotted vertically and values of time (t=1, 2, 3, . . . )plotted horizontally, for example using a least-squares fit to minimizethe sum of the squared errors in the data series.

Given a set of points in time {t}, and data values {y{t}} observed forthose points in time, values of {a} and {b} are chosen so thattrend=Σ_(t)([(at+b)−y_(t)]²) is minimized, with at+b being the trendline. The sum of squared deviations from the trend line is minimized,and constant b is an optional logical value specifying whether to forcethe constant b to equal 0.

The master data user's overall master data maturity trend score can beemployed to compare the overall trend for master data maturity from alldimensions and can be compared to each individual dimension, for exampleusing a graphic as shown at FIGS. 11A-12E. An overall score value showsstrengths and weaknesses in master data maturity, making it easy toidentify master data maturity dimensions that can be addressed toimprove the overall trend score. This helps users identify and implementmaster data tools and practices to ensuring that specific master datadimension matching is improved, thereby improving the overall masterdata strategy and master data technology and architecture of theirenterprise. Thus an improvement in master data maturity scorescorrelates directly with an improvement in the enterprise master dataand master data architecture itself, including improvements in specificmaster data dimension maturity scores to master data scores calculatedtherefrom, including goal scores, overall scores, and scores forhierarchical entities (e.g.: the master data user, lines of business,and the enterprise).

In at least one of the various embodiments, the system is configured tolog or generate, for each maturity data matrix, one or more targetmaster data maturity scores. For example, as shown in FIG. 4, as themaster data user answers the questionnaire at block 306, at block 401the system generates master data dimension maturity scores for eachmaster data dimension. At block 405, the master data user can input, viathe interface, target scores for his or her master data maturity. Themaster user can then implement master data tasks and training in eachdimension to improve proficiency and compliance with master data usageand technology, thus improving the master data architecture andstructures he or she interfaces with. At block 307 the system thengenerates the master data maturity matrix including the current masterdata dimension scores, the overall master data maturity score and/or themaster data maturity target scores for each dimension. The system canalso be configured to generate an overall master data target maturityscore for the master data user, which can be a simple mean or astatistical score as described herein.

In at least one of the various embodiments, the maturity data dimensionscores, including the assessment scores and/or the target scores, can beused to generate master data maturity goal scores from the master datamaturity dimension scores for measuring the goal, for example as a trendscore as described herein or an arithmetic mean or median of the masterdata dimension scores.

Accordingly, in at least one of the various embodiments, the system isconfigured generate, for each master data maturity matrix, a targetscore for each master data dimension; a master data goal score for oneor more of the master data dimensions, and an overall master datamaturity.

The system can then generate and display one or more maturity matrixgraphics to the master data user, for example as shown in the userinterfaces at FIGS. 7-14B

At operation 312, the master data maturity matrix is stored in a massivemaster data user maturity database 315. As shown at operations 317 and318, the master data user 301 for whom the master data user matrix 310was generated or an enterprise user 320 given access to enterprisematrix 311 can access the master data user's maturity matrix 310 at anytime.

At block 308 in at least one of the various embodiments, the systemdetermines if a master data maturity matrix has been generated foranother unique master data user for to the same enterprise. If so, atblock 309, the system generates an enterprise master maturity matrix 311from each unique master data users' master data maturity matrix 310scores. In at least one of the various embodiments, the enterprisemaster data maturity matrix 311 is generated from the master datamaturity scores of all the unique master data users for that enterprise.In an embodiment, the system could generate a higher order hierarchicalentity matrix by calculating a mean for each dimension by summing themaster data maturity scores for each dimension generated for each masterdata user and divide the score by the number of master data users whogenerated the score. In another embodiment, the system can calculate astatistical score for the higher order hierarchical entity matrix scores(e.g.: line of business, enterprise) using a linear model to aggregatemaster data maturity scores for each dimension to obtain the higherorder hierarchical entity master data maturity scores. The statisticalscoring model can be selected from the group of linear regression,linear least-squares; or an iteratively reweighted least squares (IRLS).The resulting aggregated master data maturity dimension scores for thehigher order hierarchal entity can then be used to calculate a maturitytrend score for that higher order hierarchical entity. Thus everyhierarchical entity from the unique master data user to the entireenterprise can have its own master data maturity matrix includingmaturity dimension scores (e.g. current, target) and overall maturityscores.

In an embodiment, the enterprise master data maturity matrix 311including, for example, one or more line of business master datamaturity matrices, are calculated from the master data users' masterdata maturity scores. For example, the system is configured to log theline of business for each unique master data user for the enterprisemaster data maturity matrix 311, as well as the time when the enterprisemaster data maturity matrix 311 is created. In at least one of thevarious embodiments, each line of business master data maturity matrixis generated from the master data maturity scores of the one or moreunique master data users associated with that line of business.Accordingly, in an embodiment, the system also generates one or moremaster data maturity matrices for lines of business associated with eachmaster data user matrix 310 for the enterprise maturity matrix 311. Atoperation 314, the master data maturity matrix is stored in a massivemaster data user maturity database 315.

As described herein, any of the hierarchical entities master datamaturity matrices can be compared with one another, for example viainterfaces shown in FIGS. 7-14B.

As shown at operation 318, an enterprise user 319 or an administrativeuser 311 given access to enterprise level reports can access theenterprise maturity matrix 310 at any time, although a master data user301 may not have such access. Accordingly, in an embodiment, a user'sposition in an enterprise hierarchy may determine what access they haveto master data maturity matrix interfaces and reports. For example, amanager or master data steward for a given line of business may haveaccess to the line of business matrix (not shown), other line ofbusiness matrices, and the unique master data user maturity matrices 310for his or her line of business, but not the enterprise level maturitymatrix 311 nor the unique master data user maturity matrices 310 forother lines of business. They can also be empowered to set master datamaturity target scores independent of those calculated from master datausers' target scores. In embodiments, the enterprise can define accesslevels for users as desired.

In at least one of the various embodiments, each time a master data usergenerates a new master data maturity matrix, all linked hierarchicalentity master data maturity scores are updated as well. For example, ifa new, unique master data user generates a master data maturity matrix,the line of business master data maturity matrix linked to that user isrecalculated to include the master data user's maturity scores. Theenterprise master data maturity matrix is also recalculated and updated.Similarly, if a master data user who has previously generated one ormore master data maturity matrices generates a new master data maturitymatrix, the line of business and enterprise master data maturitymatrices linked to that user are recalculated to include the master datauser's updated, current master data maturity scores, including anyupdated target scores. The system can also be configured to generate andtrack milestones such as when a target is met and the time elapsed basedon the time of creation for the matrices that set and achieved thetarget.

In at least one of the various embodiments, the maturity matrices arecontinually stored in the master data maturity database 315 over time.For example, in an embodiment, each new and current master data maturitymatrices for the all hierarchical users can be stored in the a masterdata maturity database 315 along with prior maturity matrices for theall hierarchical users to allow tracking of maturity scores over timefor all users. In another embodiment, as each master data user'smatrices and scores are tagged with the time of creation of each matrix,the system can save the master data users' maturity scores and generatethe other hierarchical matrix scores (e.g.: line of business,enterprise) from the historical record of master data users' storedscores, the time generated, and the enterprise name and line of businesslinked or mapped to that users up to that time. Thus, the system can beconfigured to generate a historical “snapshot” of the master datamaturity across all vertical and horizontal hierarchical users for anenterprise for any given time in the past up to the present, as well asdynamically track the master data maturity over time for allhierarchical entities (e.g.: master data users, lines of business, andthe enterprise).

For example, in at least one of the various embodiments, the system isconfigured to allow each unique master data user to generate a pluralityof master data maturity matrices over time. Thus the system can trackmaster data maturity scores and score differences over time for eachunique master data user as well at the other hierarchical entities(e.g.: lines of business, the enterprise). The system can be configuredto track master data maturity matrices for each hierarchical user overtime. As described herein, in at least one of the various embodiments,the system is configured to log, for each unique master data user, theline of business, the enterprise, and a time the master data maturitymatrix is generated. For example, at block 305 the system links anenterprise name and a line of business for the master data user. Eachtime the master data user completes the master data maturity evaluationsurvey as shown at block 306, the system generates a new master datamaturity matrix for the unique master data user and identifies andstores the date and time it is created, for example as metadata or otherdata. For each subsequent master user data maturity matrix for thatunique master data user, the system updates the current maturity matrixmaturity scores, and at block 310, stores the updated maturity matrix inthe massive maturity matrix database 315. The system thus is configuredto store a time-series of master data maturity matrices and scores, thuscan track progress of the scores over each master dimension, compareprogress against target scores, track and generated score differentials,and track the time taken for level and goal achievement for each uniquemaster data user.

Thus the maturity data assessment can be taken multiple times so thateach master data user (or administrative user) can track progress notonly for themselves and their division but also overall company-wideprogress. Also, adding multiple master data users and taking theassessment several times over a period of time at a regular cadence makeit easy to pin-point the real maturity levels as well as track on goingimprovements over the course of the enterprise's master data journey.

In an embodiment, at block 309 the enterprise master data maturitymatrix as well as the corresponding line of business master datamaturity matrix is also continually updated each time a unique masterdata user generates or updates their master data maturity matrix. Asdescribed herein, hierarchical entity master data maturity matrices andscores therefor are calculated from master data user maturity scores.Thus, each time a master data user generates a new master data matrix,the system can be configured to dynamically recalculate and generate allhierarchical entity master data maturity scores across the entire systemfor up to any number of hierarchical entities, even into the thousandsor millions.

In at least one of the various embodiments, the system can be configuredto prompt or set milestones and regular assessments. For example, thesystem can be configured to allow a user, for example an administrativeuser or master data user, to set regular intervals for each master datauser generate a new master data maturity matrix and update their masterdata maturity scores.

In an embodiment, the system can also be configured to allow users todelete master data maturity matrices as well. For example, the systemcan be configured to allow a master data user to delete their masterdata maturity matrix or scores, or to allow an administrative user todelete master data matrices for the enterprise. The master data maturitysystems and system interfaces are thus configured to be highly dynamicfor enterprises of even very large sizes, for example where thousands ofemployees or agents for the enterprise are generating and updatingmaster data maturity matrices. Because the system is configured toassess and optimize master data maturity for any and up to all employeesor agents for an enterprise, the system's master data maturity engineand interfaces produce highly accurate master data maturity assessmentboth horizontally and vertically in the enterprise and for allhierarchical entities and business components over time. Similarlybecause the system can interface with all master data users across theenterprise, the entire enterprise's master data and technicalarchitecture is itself measurably improved as confirmation ofimprovement is objectively scored based on each master data users ownself-assessment from predetermined questions selected from fixed ordinallevel answers. Thus the technology provides highly accurate base datafor master data maturity, as opposed to a comparatively subjective,non-dynamic single “snapshot” in time from small sample provided by “topdown” evaluators.

For example, as shown in FIG. 15, conventional master data maturityplatforms can offer “top down” measurements of master data dimensions (yaxis) that are by evaluators (e.g. managers or outside evaluators) andnot the master data users themselves. As there is no system interface ormaster data maturity engine or tracking master data maturity database,conventional systems provide a “snapshot” of, at best, current levelsand a future level. Moreover, such measurements are much more subjectiveas they are not based on actual master data usage by master data users,and thus put one or more degrees of ignorance between the actual masterdata usage and the measurement thereof, making report less reliable andany information therefrom including subjective and imprecise. Incontrast, as shown in FIG. 16 the current master data maturity interfacecan build accurate measurements of any hierarchical level of anenterprise from actual master data usage, for example master datamaturity dimensions (y axis) master data maturity levels (x-axis), linesof business (z-axis), all of which are objectively measured anddynamically tracked in time. Also, the overall trend scoring measuresand target scores measure not just current and future levels from a“snapshot,” but instead show a current trend and a future trend overtime across all hierarchical entities both vertically and horizontallyacross the enterprise and the enterprise as a whole.

In at least one of the various embodiments, the system is configured toallow a user to compare master data maturity scores for hierarchicalentities for an enterprise with master data maturity scores for otherenterprises using the system. For example, master data maturity analysiscan be hosted with an entity that provides business entity informationservices or otherwise serves multiple enterprises. The master datamaturity analysis system provides a standardized approach that allowscomparisons for hierarchical entities across and between enterprises aswell as within enterprises.

It will be understood that each block of the flowchart illustration, andcombinations of blocks in the flowchart illustration, can be implementedby computer program instructions. These program instructions can beprovided to a processor to produce a machine, such that theinstructions, which execute on the processor, create means forimplementing the actions specified in the flowchart block or blocks. Thecomputer program instructions can be executed by a processor to cause aseries of operational steps to be performed by the processor to producea computer-implemented process such that the instructions, which executeon the processor to provide steps for implementing the actions specifiedin the flowchart block or blocks. The computer program instructions canalso cause at least some of the operational steps shown in the blocks ofthe flowchart to be performed in parallel. Moreover, some of the stepscan also be performed across more than one processor, such as mightarise in a multi-processor computer system or even a group of multiplecomputer systems. In addition, one or more blocks or combinations ofblocks in the flowchart illustration can also be performed concurrentlywith other blocks or combinations of blocks, or even in a differentsequence than illustrated without departing from the scope or spirit ofthe invention.

Accordingly, blocks of the flowchart illustration support combinationsof means for performing the specified actions, combinations of steps forperforming the specified actions and program instruction means forperforming the specified actions. It will also be understood that eachblock of the flowchart illustration, and combinations of blocks in theflowchart illustration, can be implemented by special purposehardware-based systems, which perform the specified actions or steps, orcombinations of special purpose hardware and computer instructions. Theforegoing example should not be construed as limiting and/or exhaustive,but rather, an illustrative use case to show an implementation of atleast one of the various embodiments.

Illustrative Client Computer

FIG. 5 shows one embodiment of Client Computer 500 that can be includedin a system implementing embodiments. Client Computer 500 can includemany more or less components than those shown in FIG. 5. However, thecomponents shown are sufficient to disclose an illustrative embodiment.Client Computer 500 can represent, for example, one embodiment of atleast one of Client Computers 102-105 of FIG. 1.

As shown in the figure, Client Computer 500 includes a processor 502 incommunication with a mass memory 526 via a bus 534. In some embodiments,processor 502 can include one or more central processing units (CPU).Client Computer 500 also includes a power supply 528, one or morenetwork interfaces 536, an audio interface 538, a display 540, a keypad542, an illuminator 544, a video interface 546, an input/outputinterface 548, a haptic interface 550, and a global positioning system(GPS) receiver 532.

Power supply 528 provides power to Client Computer 500. A rechargeableor non-rechargeable battery can be used to provide power. The power canalso be provided by an external power source, such as an alternatingcurrent (AC) adapter or a powered docking cradle that supplements and/orrecharges a battery.

Client Computer 500 can optionally communicate with a base station (notshown), or directly with another computer. Network interface 536includes circuitry for coupling Client Computer 500 to one or morenetworks, and is constructed for use with one or more communicationprotocols and technologies including, but not limited to, GSM, CDMA,TDMA, GPRS, EDGE, WCDMA, HSDPA, LTE, user datagram protocol (UDP),transmission control protocol/Internet protocol (TCP/IP), short messageservice (SMS), WAP, ultra wide band (UWB), IEEE 802.16 WorldwideInteroperability for Microwave Access (WiMax), session initiatedprotocol/real-time transport protocol (SIP/RTP), or any of a variety ofother wireless communication protocols. Network interface 536 issometimes known as a transceiver, transceiving device, or networkinterface card (NIC).

Audio interface 538 is arranged to produce and receive audio signalssuch as the sound of a human voice. For example, audio interface 538 canbe coupled to a speaker and microphone (not shown) to enabletelecommunication with others and/or generate an audio acknowledgementfor some action.

Display 540 can be a liquid crystal display (LCD), gas plasma, lightemitting diode (LED), organic LED, or any other type of display usedwith a computer. Display 540 can also include a touch sensitive screenarranged to receive input from an object such as a stylus or a digitfrom a human hand.

Keypad 542 can comprise any input device arranged to receive input froma user. For example, keypad 542 can include a push button numeric dial,or a keyboard. Keypad 542 can also include command buttons that areassociated with selecting and sending images.

Illuminator 544 can provide a status indication and/or provide light.Illuminator 544 can remain active for specific periods of time or inresponse to events. For example, when illuminator 544 is active, it canbacklight the buttons on keypad 542 and stay on while the ClientComputer is powered. Also, illuminator 544 can backlight these buttonsin various patterns when particular actions are performed, such asdialing another client computer. Illuminator 544 can also cause lightsources positioned within a transparent or translucent case of theclient computer to illuminate in response to actions.

Video interface 546 is arranged to capture video images, such as a stillphoto, a video segment, an infrared video, or the like. For example,video interface 546 can be coupled to a digital video camera, aweb-camera, or the like. Video interface 546 can comprise a lens, animage sensor, and other electronics. Image sensors can include acomplementary metal-oxide-semiconductor (CMOS) integrated circuit,charge-coupled device (CCD), or any other integrated circuit for sensinglight.

Client computer 500 also comprises input/output interface 548 forcommunicating with external devices, such as a headset, or other inputor output devices not shown in FIG. 5. Input/output interface 548 canutilize one or more communication technologies, such as USB, infrared,Bluetooth™, or the like.

Haptic interface 550 is arranged to provide tactile feedback to a userof the client computer. For example, the haptic interface 550 can beemployed to vibrate client computer 500 in a particular way when anotheruser of a computing computer is calling. In some embodiments, hapticinterface 550 can be optional.

Client computer 500 can also include GPS transceiver 532 to determinethe physical coordinates of client computer 500 on the surface of theEarth. GPS transceiver 532, in some embodiments, can be optional. GPStransceiver 532 typically outputs a location as latitude and longitudevalues. However, GPS transceiver 532 can also employ othergeo-positioning mechanisms, including, but not limited to,triangulation, assisted GPS (AGPS), Enhanced Observed Time Difference(E-OTD), Cell Identifier (CI), Service Area Identifier (SAI), EnhancedTiming Advance (ETA), Base Station Subsystem (BSS), or the like, tofurther determine the physical location of client computer 500 on thesurface of the Earth. It is understood that under different conditions,GPS transceiver 532 can determine a physical location within millimetersfor client computer 500; and in other cases, the determined physicallocation can be less precise, such as within a meter or significantlygreater distances. In one embodiment, however, client computer 500 canthrough other components, provide other information that can be employedto determine a physical location of the computer, including for example,a Media Access Control (MAC) address, IP address, or the like.

Mass memory 526 includes a Random Access Memory (RAM) 504, a Read-onlyMemory (ROM) 522, and other storage means. Mass memory 526 illustratesan example of computer readable storage media (devices) for storage ofinformation such as computer readable instructions, data structures,program modules or other data. Mass memory 526 stores a basicinput/output system (BIOS) 524 for controlling low-level operation ofclient computer 500. The mass memory also stores an operating system 506for controlling the operation of client computer 500. It will beappreciated that this component can include a general-purpose operatingsystem such as a version of UNIX, or LINUX™, or a specialized clientcommunication operating system such as Microsoft Corporation's WindowsMobile™, Apple Corporation's iOS™, Google Corporation's Android™ or theSymbian® operating system. The operating system can include, orinterface with a Java virtual machine module that enables control ofhardware components and/or operating system operations via Javaapplication programs.

Mass memory 526 further includes one or more data storage 508, which canbe utilized by client computer 500 to store, among other things,applications 514 and/or other data. For example, data storage 508 canalso be employed to store information that describes variouscapabilities of client computer 500. The information can then beprovided to another computer based on any of a variety of events,including being sent as part of a header during a communication, sentupon request, or the like. Data storage 508 can also be employed tostore social networking information including address books, buddylists, aliases, user profile information, or the like. Further, datastorage 508 can also store message, we page content, or any of a varietyof user generated content. At least a portion of the information canalso be stored on another component of client computer 500, including,but not limited to processor readable storage media 530, a disk drive orother computer readable storage devices (not shown) within clientcomputer 500.

Processor readable storage media 530 can include volatile, nonvolatile,removable, and non-removable media implemented in any method ortechnology for storage of information, such as computer- orprocessor-readable instructions, data structures, program modules, orother data. Examples of computer readable storage media include RAM,ROM, Electrically Erasable Programmable Read-only Memory (EEPROM), flashmemory or other memory technology, Compact Disc Read-only Memory(CD-ROM), digital versatile disks (DVD) or other optical storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other physical medium which can be usedto store the desired information and which can be accessed by acomputer. Processor readable storage media 530 can also be referred toherein as computer readable storage media and/or computer readablestorage device.

Applications 514 can include computer executable instructions which,when executed by client computer 500, transmit, receive, and/orotherwise process network data. Network data can include, but is notlimited to, messages (e.g. SMS, Multimedia Message Service (MMS),instant message (IM), email, and/or other messages), audio, video, andenable telecommunication with another user of another client computer.Applications 514 can include, for example, browser 518, and otherapplications 520. Other applications 520 can include, but are notlimited to, calendars, search programs, email clients, IM applications,SMS applications, voice over Internet Protocol (VOIP) applications,contact managers, task managers, transcoders, database programs, wordprocessing programs, security applications, spreadsheet programs, games,search programs, and so forth.

Browser 518 can include virtually any application configured to receiveand display graphics, text, multimedia, messages, and the like,employing virtually any web based language. In one embodiment, thebrowser application is enabled to employ HDML, WML, WMLScript,JavaScript, SGML, HTML, XML, and the like, to display and send amessage. However, any of a variety of other web-based programminglanguages can be employed. In one embodiment, browser 518 can enable auser of client computer 500 to communicate with another networkcomputer, such as Master Data Maturity Analytics Server Computer 112and/or Business Entity Information Server Computer 114 of FIG. 1.

Applications 514 can also include Widget Controller 510 and one or moreWidgets 512. Widgets 512 can be collections of content provided to theclient computer by Master Data Maturity Analytics Server Computer 112.Widget Controller 510 can be a program that can be provided to theclient computer by Master Data Maturity Analytics Server Computer 112.Widget Controller 510 and Widgets 512 can run as native client computerapplications or they can run in Browser 518 as web browser basedapplications. Also, Widget Controller 510 and Widgets 512 can bearranged to run as native applications or web browser applications, orcombination thereof.

Illustrative Network Computer

FIG. 6A shows one embodiment of a network computer 600. Network computer600 can include many more or less components than those shown. Thecomponents shown, however, are sufficient to disclose an illustrativeembodiment. Network computer 600 can be configured to operate as aserver, client, peer, a host, or any other computer. Network computer600 can represent, for example Master Data Maturity Analytics ServerComputer 112 and/or Business Entity Information Server Computer 114 ofFIG. 1, and/or other network computers.

Network computer 600 includes processor 602, processor readable storagemedia 628, network interface unit 630, an input/output interface 632,hard disk drive 634, video display adapter 636, and memory 626, all incommunication with each other via bus 638. In some embodiments,processor 602 can include one or more central processing units.

As illustrated in FIG. 6, network computer 600 also can communicate withthe Internet, or some other communications network, via networkinterface unit 630, which is constructed for use with variouscommunication protocols including the TCP/IP protocol. Network interfaceunit 630 is sometimes known as a transceiver, transceiving device, ornetwork interface card (NIC).

Network computer 600 also comprises input/output interface 632 forcommunicating with external devices, such as a keyboard, or other inputor output devices not shown in FIG. 6. Input/output interface 632 canutilize one or more communication technologies, such as USB, infrared,Bluetooth™, or the like.

Memory 626 generally includes RAM 604, ROM 622 and one or more permanentmass storage devices, such as hard disk drive 634, tape drive, opticaldrive, and/or floppy disk drive. Memory 626 stores operating system 606for controlling the operation of network computer 600. Anygeneral-purpose operating system can be employed. Basic input/outputsystem (BIOS) 624 is also provided for controlling the low-leveloperation of network computer 600.

Although illustrated separately, memory 626 can include processorreadable storage media 628. Processor readable storage media 628 can bereferred to and/or include computer readable media, computer readablestorage media, and/or processor readable storage device. Processorreadable storage media 628 can include volatile, nonvolatile, removable,and non-removable media implemented in any method or technology forstorage of information, such as computer readable instructions, datastructures, program modules, or other data. Examples of processorreadable storage media include RAM, ROM, EEPROM, flash memory or othermemory technology, CD-ROM, digital versatile disks (DVD) or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other media which canbe used to store the desired information and which can be accessed by acomputer.

Memory 626 further includes one or more data storage 608, which can beutilized by network computer 600 to store, among other things,applications 614 and/or other data such as content 610. For example,data storage 608 can also be employed to store information thatdescribes various capabilities of network computer 600. The informationcan then be provided to another computer based on any of a variety ofevents, including being sent as part of a header during a communication,sent upon request, or the like. Data storage 608 can also be employed tostore messages, web page content, or the like. At least a portion of theinformation can also be stored on another component of network computer600, including, but not limited to processor readable storage media 628,hard disk drive 634, or other computer readable storage medias (notshown) within client computer 600.

Data storage 608 can include a database, text, spreadsheet, folder,file, or the like, that can be configured to maintain and store useraccount identifiers, user profiles, email addresses, IM addresses,and/or other network addresses; or the like.

In at least one of the various embodiments, Data storage 608 can includemaster data maturity database 315, which can contain master datamaturity matrix information determined from one or more master datausers. Master data maturity database 610, can include historicalinformation for master data maturity matrices and scores therefrom aswell as comparison information based on some or all of the hierarchicalmaster data entities that can be associated with the system.

In at least one of the various embodiments, data storage 608 can includebusiness entity information database, which can contain informationdetermined from one or more Business Entity Analytics Servers 114.Examples of Business Entity Analytics Servers 114 are described in U.S.Pat. No. 7,822,757, filed on Feb. 18, 2003 entitled System and Methodfor Providing Enhanced Information, and U.S. Pat. No. 8,346,790, filedon Sep. 28, 2010 and entitled Data Integration Method and System, theentirety of each of which is incorporated by reference herein.

Data storage 608 can further include program code, data, algorithms, andthe like, for use by a processor, such as processor 602 to execute andperform actions. In one embodiment, at least some of data store 608might also be stored on another component of network computer 600,including, but not limited to processor-readable storage media 628, harddisk drive 634, or the like.

Applications 612 can include computer executable instructions, which canbe loaded into mass memory and run on operating system 606. Examples ofapplication programs can include transcoders, schedulers, calendars,database programs, word processing programs, Hypertext Transfer Protocol(HTTP) programs, customizable user interface programs, IPSecapplications, encryption programs, security programs, SMS messageservers, IM message servers, email servers, account managers, and soforth. Applications 612 can also include website server 614, Master DataMaturity Engine 616, Master Data User Maturity Assessment and ScoreGeneration Module 618, Master Data Maturity Matrix Generator 620, and/orReport Generator 621.

Website server 614 can represents any of a variety of information andservices that are configured to provide content, including interfaces asdescribed herein, over a network to another computer. Thus, websiteserver 614 can include, for example, a web server, a File TransferProtocol (FTP) server, a database server, a content server, or the like.Website server 614 can provide the content including messages over thenetwork using any of a variety of formats including, but not limited toWAP, HDML, WML, SGML, HTML, XML, Compact HTML (cHTML), Extensible HTML(xHTML), or the like.

Master Data Maturity Engine 616 can be configured to generate masterdata user entity matrices and dynamic interface reports, and can beoperative and/or hosted on Master Data Maturity Analytics ServerComputer 112, Business Entity Information Server 144 or the like. MasterData Maturity Engine 616 can employ processes, or parts of processes,such as those described in conjunction with FIGS. 2-4 and interfacesFIGS. 7-14B, to perform at least some of its actions.

Master Data Maturity Engine 616 can comprise Master Data MaturityAssessment and Score Generation Module 618. In at least one of thevarious embodiments, Master Data Maturity Assessment and ScoreGeneration Module 618 can be operative and/or hosted on Master DataMaturity Analytics Server Computer 112 or Business Entity InformationServer 144 and can employ processes, or parts of processes, like thosedescribed in conjunction with FIGS. 2-4 and interfaces of FIGS. 7-10, toperform at least some of its actions.

Master Data Maturity Engine 616 can comprise Master Data Maturity MatrixGenerator 619. In at least one of the various embodiments, Master DataMaturity Matrix Generator 619 can be hosted on Master Data MaturityAnalytics Server Computer 112 or Business Entity Information Server 144and can employ processes, or parts of processes, like those described inconjunction with FIGS. 2-4 and interfaces of FIGS. 11-14, to perform atleast some of its actions.

Master Data Maturity Engine 616 can comprise Comparator 620. In at leastone of the various embodiments, Master Data Maturity Matrix Generator619 can be operative and/or hosted on Master Data Maturity AnalyticsServer Computer 112 and can employ processes, or parts of processes,such as those described in conjunction with FIGS. 2-4 and interface ofFIG. 14B, to perform at least some of its actions.

Master Data Maturity Engine 616 can comprise a Report Generator 621. Inat least one of the various embodiments, Report Generator 621 can bearranged and configured to determine and/or generate reports and dynamicgraphic objects based on the master data maturity matrices. In at leastone of the various embodiments, Report Generator 621 can be operativeand/or hosted on Master Data Maturity Analytics Server Computer 112 andcan employ processes, or parts of processes, like those described inconjunction with FIGS. 2-4 and interfaces of FIGS. 11A-14B, to performat least some of its actions

In at least one of the various embodiments, a master data maturityengine component 616 can be arranged to generate a master data maturitymatrix based on the ordinal inputs from a master data user. In at leastone of the various embodiments, master data maturity analysis caninclude determining the scores for the master data dimensions and goalsas described herein.

Logical System Architecture

FIG. 6B represents a logical architecture flow for system 650 for masterdata maturity analytics in accordance with at least one of the variousembodiments.

In at least one of the various embodiments, master data maturityanalysis, inputs from unique master data users 201-1, 201-2, 201-3,201-N, are input to master data Master Data Maturity Assessment andScore Generation Module 618 can be operative and/or hosted on MasterData Maturity Analytics Server Computer 112 and can employ processes, orparts of processes, like those described in conjunction with FIGS. 2-4and interfaces of FIGS. 7-10J, to perform at least some of its actions.

In at least one of the various embodiments, master data maturityanalysis can include determining historical information from a recordstore, such as, master data maturity database 610. In at least one ofthe various embodiments master data maturity database 610 can includemaster data user information, master data maturity matrix informationincluding master data maturity scores and time of generation,hierarchical master data entity information, information, or the like,associated with hierarchical master data entities. Thus, in at least oneof the various embodiments, master data maturity database 610 can be astorehouse of historical master data maturity matrices and master datamaturity information that is associated with hierarchical master datausers.

In at least one of the various embodiments, master data maturityanalysis can include obtaining and linking or mapping hierarchicalentity information for an enterprise from a record store, such as,enterprise database 611. In at least one of the various embodiments,enterprise database includes a database of robust company/businessentity data 611 and employee data to enrich company master data maturitydatabases as described herein. Examples of Business Entity AnalyticsServers 104 are described in U.S. Pat. No. 7,822,757, filed on Feb. 18,2003 entitled System and Method for Providing Enhanced Information, andU.S. Pat. No. 8,346,790, filed on Sep. 28, 2010 and entitled DataIntegration Method and System, the entirety of each of which isincorporated by reference herein. Enterprise database 611 can includefirmographic data including company names, company IDs, employeelistings and contact information, company scores, line of business andcategories therefor, and robust linkage to firmographic data forenterprises. Database 611 can also include any number of categoriesapplicable across enterprises that can be used to define hierarchalentities to categorize master data users, for example lines of business,vertical or horizontal business modules, department titles, employeetitles, firmographic categories, and so on.

In at least one of the various embodiments, master data maturity matrixinformation from master data maturity engine component 616 can flow tocomparator 620 and/or report generator 619. In at least one of thevarious embodiments, report generator 619 can be arranged to generateone or more reports based on the master data maturity matrixinformation. In at least one of the various embodiments, reports caninclude historical master data maturity matrix information or comparisoninformation as described herein. In at least one of the variousembodiments, reports can be determined and formatted as graphicinterface objects, for example as described with respect to FIGS. 7-14B.

In at least one of the various embodiments, a graphic display interfacecan render a display of the information produced by the other componentsof the systems, for example via the website server 612. In at least oneof the various embodiments, display 570 can be presented on a clientcomputer accessed over network, such as client computers 102, 103, 104,105, 500 or the like.

Illustrative User Interface Use Cases

FIGS. 7-14B represent graphical user interfaces for master data maturityanalysis and optimization with at least one of the various embodiments.In at least one of the various embodiments, user interfaces other thanuser interfaces those described below can be employed without departingfrom the spirit and/or scope of the claimed subject matter. Such userinterfaces can have more or fewer user interface elements, which can bearranged in various ways. In some embodiments, user interfaces can begenerated using web pages, mobile applications, emails, or the like. Inat least one of the various embodiments, master data maturity engine andcomponents, or the like, can include processes and/or API's forgenerating user interfaces.

As shown in FIG. 7, the system interface presents a user interface amaster data evaluation. As described herein, the survey comprises, foreach master data dimension a predetermined question and a plurality ofpredetermined ordinal answers for each question, and the user interfaceis configured to accept a selected answer as the ordinal measure input.The interface includes a Name field where the master data user entershis or her name, as well as a required Job Function input, which isselected from a drop-down menu of predetermined job functions:(Information Technology, Supply Chain/Procurement/Manufacturing, Salesand Marketing, Finance, Executive). The required predefined Job Functionselections can be used to identify and link the master data user's Lineof Business. The interface also includes a field for further jobdescriptions, which can be used to further refine and identify masterdata usage areas and quality by linking the scores to the addedinformation provided by master data users. The interface includesanother dropdown menu for selecting the type of assessment. The user canchoose to assess and generate scores for his or her current master datamaturity assessment (Current), set target scores for his or her futuremaster data maturity (Target), or assess and generate scores for bothcurrent and target master data maturity (Current and Target).

Once the master data user enters at least the required information andtype of assessment, the graphic user interface presents an interfaceobject for answering predetermined questions and generating master datamaturity scores and/or master data target scores as described herein.For example, as shown in FIGS. 7-101 the interface comprises master datagoal tabs, and when the tabs are selected via the interface, the systemprovides one or more master data dimension questions and 6 predefinedanswers therefor the user can select. In the embodiment, the master datauser is provided with an interactive graphic including a slider thatslides along the preselected answers and the ordinal levels, for exampleas shown in FIGS. 7-9B however any entry input for the interface can beused. For example, graphic can be configured with each of the ordinalanswers as a clickable object, a radio object or other input. Forexample, as shown in FIGS. 10A-10J, the interactive graphic includes,for each question, a row or rows including a series of clickable boxesalong the preselected answers and the ordinal levels for the question,and the user can click into the box to generate the score. As the masterdata user selects the preselected answer via the interface, the systemgenerates and shows the master data dimension score for the selectedlevel in a Score field. Once the user has made all the selections foreach goal, the user clicks on a “Next” button and proceeds to the nextgoal.

As shown in FIGS. 9A-10J, the interface also includes an input objectfor inputting a target score for each master data dimension. If themaster data user has selected “Target” (FIG. 9A) or “Current and Target”(FIGS. 9B, 10A-10J) in the dropdown assessment menu, the master datauser is presented with interfaces that include an input object forgenerating target master data maturity scores. The system can beconfigured to require the master data user generate current master datamaturity scores prior to or in conjunction with generating targetscores. The system can also be configured to not allow a user to set alower target score than a current score so that the target score isalways at least equal to the current master data maturity score at anygiven time. For example, as shown in FIG. 9B, a left hand Currentassessment slider that slides along a line for the preselected answersand the ordinal levels is employed, while on the right hand of the sameline, a Target slider is employed, where the scores increase from leftto right. In another embodiment, the system can be configured to allowthe user to enter the Current and Target scores separately, for exampleas shown in FIGS. 10A-10J, where the interactive graphic includes tworows for each question—a Current row and Target row—each row includingseries of clickable boxes along the preselected answers and the ordinallevels for each question, and the user can click into the box in boththe Current and Target rows to generate the scores.

Once the master data user completes the master data maturity survey, allthe master data dimension scores are generated and, if selected, targetmaster data dimension scores. The system can calculate an overallmaturity score from the master data maturity dimension scores. Thesystem can also generate a graphic showing a master data maturity reportfor the master data user.

For example, as shown in FIGS. 11A-13, the master data maturity matrixgraphic comprises a radar chart for the master data user's master datamaturity scores. The radar chart comprises a plurality of equiangularradii, each radii representing one of the master data maturitydimensions, and the corresponding master data maturity score generatedby the system for the dimension is plotted on each radii along thestandardized interval scale measuring the ordinal level for thedimension. The plotted dimension scores are connected to define astar-like chart or circular shape within the chart. In an embodiment,the overall master data maturity trend score can be plotted at the sameinterval on all the radii and to define a circular shape within thechart or can simply be shown as a number value.

The radar chart forms a two-dimensional chart of three or morequantitative variables represented on axes starting from the same point.It consists of a sequence of equi-angular spokes (radii), with eachspoke representing one of the ordinal levels and score of the maturitydimension. The data length of a spoke is proportional to the magnitudeof the level interval for the data point relative to the maximummagnitude of the variable across all data points. A line is drawnconnecting the data values for each spoke.

The radar chart graphic shows the observations that are most similar andclusters of observations, as well as examines relative values for asingle data point (e.g., point 3 is large for variables 2 and 4, smallfor variables 1, 3, 5, and 6) and locates similar points or dissimilarpoints. The radar graphic displays multivariate observations with anarbitrary number of variables, where each star represents a singleobservation.

The master data user's score is represented by the star-shaped line andhighlights areas of low score, which in turn impacts the “MaturityTrend” score. In an embodiment, as the Maturity Trend score is a singlevalue calculated from the master data maturity dimension scores, theMaturity Trend score can be shown as a circle that has the same intervalvalue for all dimensions as shown in FIG. 11A. The graph shows masterdata strengths and weaknesses, making it easy to identify dimensionsthat can be addressed to improve the overall trend score. This helpsusers identify and invest effort in ensuring that matching is improvedin order to help with the overall master data strategy of theirorganization.

As shown in FIGS. 12A-12E, the systems can also generate plotted masterdata dimension target maturity scores on the radar chart. When showntogether with the current master data maturity dimension scores, thegraphic shows a larger star-like or circular chart that surrounds or iscoincident with the current scores, visually identifying a precise,quantified delta for each master data dimension as well as visuallyrepresenting an overall area delta for master data improvement thatcorresponds to that master data user's technical contribution to improvethe enterprise's master data. As shown at FIGS. 12A-12E, the report canalso include a detailed breakdown of dimensions comparisons betweencurrent and target scores. The report can also show the Maturity Trendscore.

As shown in FIG. 13, as a plurality of master data users across theenterprise each generate one or more master data maturity matrices, thesystem is configured to generate a master data maturity matrix for theentire enterprise (all company reports). In an embodiment, the systemcan generate a master data maturity graphic for the enterprise, forexample such as radar charts like those generated for individual masterdata users. In an embodiment, master data maturity graphics can also begenerated for lines of business within the enterprise, for example radarcharts or bar graphs for each line of business, which can be generatedand displayed independently of the maturity matrix for the entireenterprise, together with it, or both.

As noted herein, in order to assess real master data maturity of anorganization, it is best to take “sampling” from multiple‘points’—master data users—within the company as opposed to a singleperson taking the assessment. One person taking the assessment can giveskewed results and may not represent an accurate maturity level for thewhole company or even division or line of business. Assessments can betaken by master data users representing divisions or groups,practitioners, decision makers, end users, etc. All the master data userscores are used to calculate and generate company-wide maturity trendscores while allowing for identifying and comparing divisional/grouplevel maturity assessments.

In at least one of the various embodiments, the system can generatereports or graphics for comparing the master data maturity ofhierarchical master data entities such as lines of business, master datauser entities, and an enterprise. For example, as shown in FIGS. 14A-14Bthe master data maturity graphic can include a bar graph, with a firstaxis marking dimensions and a second axis being the standardizedinterval scale measuring the ordinal level for each dimension on thefirst axis, and the graphic is configured such that hierarchicalentities' dimension scores are plotted and compared on the bar graph. Asshown in FIG. 14B, the master data maturity scores for three lines ofbusiness are plotted on the bar graph: Information Technology, Sales andMarketing, and Supply Chain/Procurement/Manufacturing. Supply Chain ITscores high on Monitoring but Sales and Marketing master data userscores aggregate to show a monitoring master data dimension as lowmaturity. Thus, Sales and Marketing master data users are not meetingmaster data monitoring goals with the current processes in place and isan area that can be addressed and improved.

Although the master data maturity comparison graphic as shown comparesline of business entities, the system can be configured to compere anyhierarchical master data entities. For example, the bar graph could plota master data user's maturity data dimension scores, the master datamaturity dimension scores for his or her line of business, and theenterprise master data maturity score. Or, for another example, one ormore line of business master data maturity matrix scores can be plottedand compared to the enterprise's master data maturity matrix scores.

In an embodiment, the computer system can be configured to compareentities from different enterprises. For example, the system can beconfigured to allow an enterprise to compare the hierarchical entitymaster data maturity matrices generated from its master data users withmaster data maturity matrices from other enterprises stored in themaster user database. For example, in at least one of the variousembodiments, the system can allow users to compare matrices and mastermaturity scores for peer hierarchal entities in other enterprises, suchas maturity matrices for lines of business or other defined businesscomponents As described herein, master data user maturity matrices canbe linked and mapped firmographic data, for example from a BusinessEntity Server Information Server as described herein. Such data can beprocessed and presented to preserve business entity anonymity, and/orenterprises may opt to share such data openly.

Another embodiment includes a system for assessing and optimizing masterdata maturity for an enterprise master data comprising: a memoryincluding a database configured to store master data maturity data for aplurality of hierarchical entities including a plurality of uniquemaster data users; a master data maturity engine configured to measuremaster data maturity for a plurality of predefined hierarchical entitiesfor an enterprise based on master data maturity units generated for aplurality of unique master data users, the master data maturity engineconfigured to generate, in the computer system, a master data maturitymatrix comprising a plurality of master data dimensions; wherein thesystem is configured to generate, in the computer system, the masterdata maturity matrix for each of the plurality of hierarchical entities;wherein the master data maturity engine is configured to generate, inthe computer system, a master data maturity unit for each of theplurality of master data dimensions; wherein an enterprise master datamaturity matrix is generated from the master data maturity units of allthe unique master data users for that enterprise; and a user interfaceconfigured to present a plurality of unique master data users with amaster data evaluation interface for master data user input to themaster data maturity engine and to display the master data maturitymatrix.

Furthermore, each master data dimension maturity unit generated by themaster data maturity engine is identified to a standardized intervalscale corresponding to a plurality of ordinal levels for each dimension.

The system is configured to calculate an overall master data maturityfrom the plurality of master data dimension maturity units for themaster data maturity matrix.

The hierarchal entities include a line of business and the enterprise,and a line of business master data maturity matrix and an enterprisemaster data maturity matrix are calculated from the master data usermaster data maturity units, wherein each line of business master datamaturity matrix is generated from the master data maturity units of theone or more unique master data users identified to that line ofbusiness.

The system is configured to allow a user to compare master data maturitymatrices for the hierarchical entities.

The master data evaluation interface is configured to present aninteractive survey comprising, for each master data dimension, an inputfor an ordinal measure of the dimension, wherein the master datamaturity engine generates the master data maturity unit for the masterdata dimension from the ordinal measure input.

The master data maturity matrix includes a plurality of master datagoals, each master data goal comprising one or more of the master datadimensions. The master data maturity matrix includes a plurality ofmaster data goals, each master data goal comprising one or more of themaster data dimensions. The overall master data maturity is a masterdata maturity trend. The master data maturity trend is generated by thecomputer system from a statistical model.

The statistical model is selected from a linear model selected from thegroup of linear regression, linear least-squares; and an iterativelyreweighted least squares (IRLS).

The system is configured to log or generate, for each maturity datamatrix, one or more target master data maturity units. The system isconfigured to log or generate a target unit for each master datadimension; each master data goal, and an overall master data maturity.The system is configured to allow each unique master data user togenerate a plurality of master data maturity matrices over time to trackmaster data maturity differences. The system is configured to trackmaster data maturity matrices for each hierarchical user over time.

The enterprise master data maturity matrix is continually updated eachtime a unique master data user generates or updates their master datamaturity matrix.

The system is configured to log, for each unique master data user, theline of business, the enterprise, and a time the master data maturitymatrix is generated.

The system is configured to allow a user to compare master data maturitymatrix data for hierarchical entities for an enterprise with master datamaturity matrix data for other enterprises using the system.

The line of business master data maturity matrix is continually updatedeach time a unique master data user generates or updates their masterdata maturity matrix.

The master data evaluation survey comprises, for each master datadimension a predetermined question and a plurality of predeterminedordinal answers for each question, and the user interface is configuredto accept a selected answer as the ordinal measure input.

The master data maturity matrix interface display comprises a graphicincluding a radar chart comprising a plurality of equiangular radii,each radii representing one of the master data dimensions, and thecorresponding master data maturity unit for the dimension is plotted oneach radii along the standardized interval scale measuring the ordinallevel for the dimension; wherein the plotted dimension units areconnected to define a star-like chart or circular shape within thechart.

The master data maturity matrix interface comprises a graphic includinga bar graph, with a first axis marking dimensions and a second axisbeing the standardized interval scale measuring the ordinal level foreach dimension on the first axis, and the graphic is configured suchthat hierarchical entities' dimension matrices are plotted and comparedon the bar graph. The bar graph is configured to compare lines ofbusiness, master data user entities, and an enterprise. The bar graph isconfigured to compare entities from different enterprises.

A method of assessing and optimizing master data maturity the methodbeing performed by a computer system that comprises one or moreprocessors, a memory operatively coupled to at least one of theprocessors, and a computer-readable storage medium encoded withinstructions executable by at least one of the processors andoperatively coupled to at least one of the processors, the methodcomprising: presenting the plurality of unique master data users with amaster data maturity interface configured to generate a master datamaturity matrix comprising a plurality of master data dimensions foreach unique master data user; presenting, via the interface, each uniquemaster data user with a master data evaluation interface for master datainput to the master data maturity engine; generating the master datamaturity units for each of the plurality of master data dimensions fromthe ordinal measure input; generating, for each of the unique masterdata users, a master data maturity matrix comprising the plurality ofmaster data dimension maturity units and an overall master datamaturity; logging the time the master data maturity matrix is created;determining if a master data maturity matrix has been generated foranother master data user master data user logged to the same enterprise;if so, generating an enterprise master maturity matrix from each uniqueusers' master data maturity matrix units; logging the time theenterprise master data maturity matrix is created; and storing eachmaster data maturity matrix in the massive user database.

The method further comprises: associating each of the unique master datausers with a line of business and an enterprise. The method furthercomprises: logging the line of business for each unique master data userto enterprise master data maturity matrix. The method further comprises:presenting, via the master data evaluation interface, a surveycomprising, for each master data dimension, an input for an ordinalmeasure of the dimension, wherein the master data maturity enginegenerates the master data maturity matrix for the master data dimensionfrom the ordinal measure input.

A computer program product comprising a computer-readable storage mediumencoded with instructions that, when executed by at least one processorwithin a computer system that comprises one or more processors and amemory operatively coupled to at least one of the processors, cause thecomputer system at least to: present the plurality of unique master datausers with a master data maturity interface configured to generate amaster data maturity matrix comprising a plurality of master datadimensions for each unique master data user; present, via the interface,each unique master data user with a master data evaluation interface formaster data input to the master data maturity engine; generate themaster data maturity units for each of the plurality of master datadimensions from the ordinal measure input; generate, for each of theunique master data users, a master data maturity matrix comprising theplurality of master data dimension maturity units and an overall masterdata maturity; log the time the master data maturity matrix is created;determine if a master data maturity matrix has been generated foranother master data user master data user logged to the same enterprise;if so, generate an enterprise master maturity matrix from each uniqueusers' master data maturity matrix units; log the time the enterprisemaster data maturity matrix is created; and storing each master datamaturity matrix in the massive user database.

Furthermore, wherein the instructions, when executed by at least oneprocessor, further cause the computer system at least to: associate eachof the unique master data users with a line of business and anenterprise. The instructions, when executed by at least one processor,further cause the computer system at least to: log the line of businessfor each unique master data user to enterprise master data maturitymatrix.

The instructions, when executed by at least one processor, further causethe computer system at least to: present, via the master data evaluationinterface, a survey comprising, for each master data dimension, an inputfor an ordinal measure of the dimension, wherein the master datamaturity engine generates the master data maturity matrix for the masterdata dimension from the ordinal measure input.

The techniques described herein are exemplary, and should not beconstrued as implying any particular limitation on the presentdisclosure. It should be understood that various alternatives,combinations and modifications could be devised by those skilled in theart. For example, steps associated with the processes described hereincan be performed in any order, unless otherwise specified or dictated bythe steps themselves. The present disclosure is intended to embrace allsuch alternatives, modifications and variances that fall within thescope of the appended claims.

What is claimed is:
 1. A system for assessing and optimizing master datamaturity comprising: a network computer, including: a transceiver forcommunicating over the network; a massive user database for anenterprise configured to store master data maturity data for a pluralityof hierarchical entities for the enterprise including a plurality ofunique master data users; and a memory for storing at leastinstructions; a processor device that is operative to executeinstructions that enable actions, including: present the plurality ofunique master data users with a master data maturity graphical userinterface operatively connected to the massive user database configuredto generate a master data maturity matrix comprising a plurality ofmaster data dimensions for each unique master data user logged to thesystem; present, via the graphical user interface, each unique masterdata user with a master data evaluation interface for master data inputto the master data maturity engine wherein the graphical user interfacecomprises an interactive graphic input object to set a predeterminedcurrent ordinal measure for each of the plurality of master datadimensions and an interactive graphic input object to set apredetermined target ordinal measure for each of the plurality of masterdata dimensions, wherein the interactive graphic input to set frominputting an ordinal measure that is lower than the ordinal measure setfor the respective master data dimension; generate master data maturityunits for each of the plurality of master data dimensions from thecurrent ordinal measure input and the target ordinal measure input;generate, for each of the unique master data users, a current masterdata maturity matrix comprising the plurality of master data dimensionmaturity units and an overall master data maturity; generate, for eachof the unique master data users, a target master data maturity matrixcomprising the plurality of master data dimension maturity units and anoverall target master data maturity; log the time the current masterdata maturity matrix and the target master data maturity matrix iscreated; determine if a current master data maturity matrix has beengenerated for another master data user logged to the same enterpriselinked to the each of the master data users in the massive userdatabase; if so, automatically generate or update an enterprise mastermaturity matrix from each unique users' master data maturity matrix; logthe time the enterprise master data maturity matrix is created; storeeach master data maturity matrix and time logs therefor in the massiveuser database, wherein the system is configured to dynamicallyrecalculate and generate all hierarchical entity master data maturityscores across the entire system for the plurality hierarchical entities.2. The computer system of claim 1, wherein the one or more processorsare further programmed to associate each of the unique master data userswith a line of business and an enterprise.
 3. The computer system ofclaim 2, wherein the one or more processors are further programmed tolog the line of business for each unique master data user to enterprisemaster data maturity matrix.
 4. The computer system of claim 1, whereinthe one or more processors are further programmed to present, via themaster data evaluation interface, a survey comprising, for each masterdata dimension, an input for an ordinal measure of the dimension,wherein the master data maturity engine generates the master datamaturity matrix for the master data dimension from the ordinal measureinput.
 5. The computer system of claim 1, wherein the one or moreprocessors are further programmed at least to: log or generate, for eachmaturity data matrix, one or more target master data maturity units. 6.The computer system of claim 1, wherein the one or more processors arefurther programmed at least to: log or generate, for each master datamaturity matrix, a target unit for each master data dimension; a masterdata goal for one or more of the master data dimensions, and an overallmaster data maturity.
 7. The computer system of claim 1, wherein the oneor more processors are further programmed at least to: display, via themaster data maturity interface, a graphic of the master data maturitymatrix.
 8. The computer system of claim 7, wherein the one or moreprocessors are further programmed at least to: display, via the masterdata maturity interface, the master data maturity matrix graphiccomprising a radar chart comprising a plurality of equiangular radii,each radii representing one of the master data dimensions, and thecorresponding master data maturity unit for the dimension is plotted oneach radii along a standardized interval scale measuring the ordinallevel for the dimension; and wherein the plotted dimension units areconnected to define a star-like chart or circular shape within thechart.
 9. The computer system of claim 8, wherein the one or moreprocessors are further programmed at least to: plot and display one ormore corresponding master data maturity target units for the dimensionalong a standardized interval scale measuring the ordinal level for thedimension; wherein the plotted data maturity targets units are connectedto define a star-like chart or circular shape within the chart.
 10. Thecomputer system of claim 7, wherein the one or more processors arefurther programmed at least to: display, via the master data maturityinterface, the master data maturity matrix graphic comprising a bargraph, with a first axis marking dimensions and a second axis being thestandardized interval scale measuring an ordinal level for eachdimension on the first axis, and the graphic is configured such that oneor more hierarchical entities' dimension units are plotted and comparedon the bar graph.
 11. The computer system of claim 10, wherein the oneor more processors are further programmed at least to: compare masterdata maturity matrices of hierarchical entities selected from: one ormore lines of business, master data user entities, and an enterprise;and display the comparisons on the bar graph.
 12. The computer system ofclaim 11, wherein the one or more processors are further programmed atleast to: compare master data maturity matrix data of entities fromdifferent enterprises and; display the comparisons on the bar graph. 13.The computer system of claim 1, wherein the one or more processors arefurther programmed at least to: generate, for each unique master datauser, a plurality of master data maturity matrices over time; and track,for the plurality of master data maturity matrices for the unique masterdata user, master data maturity matrix differences, wherein the systemis configured to track master data maturity matrices for eachhierarchical user over time.
 14. The computer system of claim 4, whereinthe one or more processors are further programmed at least to: present,via the interface, the master data evaluation survey comprising, foreach master data dimension, a predetermined question and a plurality ofpredetermined ordinal answers for each question, and the user interfaceis configured to accept a selected answer as the ordinal measure input.15. The computer system of claim 11, further comprising: an operativeconnection to a massive business entity information database configuredto store firmographic data for a plurality of enterprises; and whereinthe one or more processors are further programmed at least to: registerthe enterprise to the massive business entity database; and access themassive business entity database to log, for each of the unique masterdata users, firmographic data for the enterprise the master data user isassociated with.
 16. A computer system for assessing and optimizingmaster data maturity for an enterprise master data comprising: a memoryincluding an enterprise massive user database configured to store masterdata maturity data for the enterprise for a plurality of hierarchicalentities including a plurality of unique master data users; a masterdata maturity engine configured to measure master data maturity for aplurality of predefined hierarchical entities for an enterprise based onmaster data maturity units generated for the plurality of unique masterdata users, the master data maturity engine configured to generate, inthe computer system, a master data maturity matrix comprising aplurality of master data dimensions; wherein the system is configured togenerate or dynamically update, in the computer system, the master datamaturity matrix for each of the plurality of hierarchical entities;wherein the master data maturity engine is configured to generate, inthe computer system, a master data maturity unit for each of theplurality of master data dimensions; wherein an enterprise master datamaturity matrix is generated from the master data maturity units of allthe unique master data users linked to that enterprise in the massiveuser database; and a graphical user interface operative connected to themassive user database configured to present a plurality of unique masterdata users with a master data evaluation interface for master data userlogged to the system to input to the master data maturity engine and todisplay the master data maturity matrix, wherein the graphical userinterface comprises an interactive graphic input object to set apredetermined current ordinal measure for each of the plurality ofmaster data dimensions and an interactive graphic input to set apredetermined target ordinal measure for each of the plurality of masterdata dimensions, wherein the interactive graphic input object to set thepredetermined target ordinal measure is configured to prevent eachmaster data user from inputting an ordinal measure that is lower thanthe ordinal measure set for the respective master data dimension.
 17. Amethod of assessing and optimizing master data maturity the method beingperformed by a computer system that comprises one or more processors, amemory operatively coupled to at least one of the processors, and acomputer-readable storage medium encoded with instructions executable byat least one of the processors and operatively coupled to at least oneof the processors, the method comprising: presenting, on a graphicaluser interface, a plurality of unique master data users logged to thesystem with a master data maturity interface operatively connected to amassive user database and configured to generate a master data maturitymatrix comprising a plurality of master data dimensions for each uniquemaster data user, wherein the graphical user interface comprises aninteractive graphic input to set a predetermined current ordinal measurefor each of the plurality of master data dimensions and an interactivegraphic input to set a predetermined target ordinal measure for each ofthe plurality of master data dimensions, wherein the interactive graphicinput to set the predetermined target ordinal measure is configured toprevent each master data user from inputting an ordinal measure that islower than the ordinal measure set for the respective master datadimension; presenting, via the graphical user interface, each uniquemaster data user with a master data evaluation interface for master datainput to the master data maturity engine; generating a plurality ofmaster data maturity units for each of the plurality of master datadimensions from the current ordinal measure input and the target ordinalmeasure input; generating, for each of the unique master data users, acurrent master data maturity matrix comprising the plurality of masterdata dimension maturity units and an overall master data maturity;generate, for each of the unique mater data users, a target master datamaturity matrix comprising the plurality of master data dimensionmaturity units and an overall target master data maturity; logging thetime the current master data maturity matrix and the target master datamaturity matrix is created; determining if a current master datamaturity matrix has been generated for another master data user loggedto a same enterprise and linked to the each of the unique master datausers in a massive user database; if so, automatically generating orupdating an enterprise master maturity matrix from each unique users'master data maturity matrix units; logging the time the enterprisemaster data maturity matrix is created; and storing each master datamaturity matrix and time logs therefor in the massive user database. 18.A computer program product comprising a non-transitory computer-readablestorage medium encoded with instructions that, when executed by at leastone processor within a computer system that comprises one or moreprocessors and a memory operatively coupled to at least one of theprocessors, cause the computer system at least to: present, via agraphical user interface, a plurality of unique master data users with amaster data maturity interface operatively connected to the massive userdatabase for an enterprise configured to generate a master data maturitymatrix comprising a plurality of master data dimensions for each uniquemaster data user logged to the system; present, via the graphical userinterface, each unique master data user with a master data evaluationinterface for master data input to the master data maturity engine,wherein the graphical user interface comprises an interactive graphicinput object to set a predetermined current ordinal measure for each ofthe plurality of master data dimensions and an interactive graphic inputobject to set a predetermined target ordinal measure for each of theplurality of master data dimensions, wherein the interactive graphicinput master data user from inputting an ordinal measure that is lowerthan the ordinal measure set for the respective master data dimension;generate the master data maturity units for each of the plurality ofmaster data dimensions from the current ordinal measure input and thetarget ordinal measure input; generate, for each of the unique masterdata users, a master data maturity matrix comprising the plurality ofmaster data dimension maturity units and an overall master datamaturity; generate, for each of the unique master data users, a targetmaster data maturity matrix comprising the plurality of master datadimension maturity units and an overall target master data maturity; logthe time the current master data maturity matrix and the target masterdata maturity matrix is created; determine if a master data maturitymatrix has been generated for another master data user logged to thesame enterprise and linked to the each of the unique master data usersin a massive user database; if so, automatically generate or update anenterprise master maturity matrix from each unique users' master datamaturity matrix units; log the time the enterprise master data maturitymatrix is created; and storing each master data maturity matrix and timelogs therefor in the massive user database, wherein the system isconfigured to dynamically recalculate and generate all hierarchicalentity master data maturity scores across the entire system for theplurality hierarchical entities.